In this project, you'll use generative adversarial networks to generate new images of faces.
You'll be using two datasets in this project:
Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.
If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".
data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
"""
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"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Downloading mnist: 9.92MB [00:01, 9.00MB/s] Extracting mnist: 100%|██████████| 60.0k/60.0k [00:05<00:00, 11.6kFile/s] Downloading celeba: 1.44GB [08:14, 2.92MB/s]
Extracting celeba...
show_n_images = 25
"""
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"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
<matplotlib.image.AxesImage at 0x7ff5ab20ddd8>
The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.
show_n_images = 25
"""
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"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
<matplotlib.image.AxesImage at 0x7ff5ab4e6400>
Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.
The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).
You'll build the components necessary to build a GANs by implementing the following functions below:
model_inputsdiscriminatorgeneratormodel_lossmodel_opttrainThis will check to make sure you have the correct version of TensorFlow and access to a GPU
"""
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"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.8.0 Default GPU Device: /device:GPU:0
Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:
image_width, image_height, and image_channels.z_dim.Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)
import problem_unittests as tests
def model_inputs(image_width, image_height, image_channels, z_dim):
"""
Create the model inputs
:param image_width: The input image width
:param image_height: The input image height
:param image_channels: The number of image channels
:param z_dim: The dimension of Z
:return: Tuple of (tensor of real input images, tensor of z data, learning rate)
"""
# TODO: Implement Functio
real_input = tf.placeholder(tf.float32, shape=[None, image_height, image_height, image_channels], name='real_input')
z_data = tf.placeholder(tf.float32, shape=[None, z_dim])
learning_rate = tf.placeholder(tf.float32)
return real_input, z_data, learning_rate
"""
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"""
tests.test_model_inputs(model_inputs)
Tests Passed
Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).
def discriminator(images, reuse=False):
"""
Create the discriminator network
:param images: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
"""
# TODO: Implement Function
with tf.variable_scope('discriminator', reuse=reuse):
# Input is an image: 28x28x3(or 1)
c1 = tf.layers.conv2d(images, 64, [5,5], [2,2], activation=None, padding='same')
c1 = tf.maximum(0.2*c1, c1)
# Input is 14x14x64
c2 = tf.layers.conv2d(c1, 128, [5,5], [2,2], activation=None, padding='same')
c2 = tf.layers.batch_normalization(c2, training=True)
c2 = tf.maximum(0.2*c2, c2)
# Input is 7x7x128
c3 = tf.layers.conv2d(c2, 256, [5,5], [2,2], activation=None, padding='same')
c3 = tf.layers.batch_normalization(c3, training=True)
c3 = tf.maximum(0.2*c3, c3)
# Input is 4x4x256
c3_flat = tf.reshape(c3, [-1, 4*4*256])
logits = tf.layers.dense(c3_flat, 1)
output = tf.sigmoid(logits)
return output, logits
"""
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"""
tests.test_discriminator(discriminator, tf)
Tests Passed
Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.
def generator(z, out_channel_dim, is_train=True):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
:param is_train: Boolean if generator is being used for training
:return: The tensor output of the generator
"""
reuse = not is_train
with tf.variable_scope('generator', reuse=reuse):
# First layer is fully connected.
layer_size = 7*7*512
x = tf.layers.dense(inputs=z, units=layer_size)
# Reshape into a convolutional layer.
c1 = tf.reshape(x, [-1, 7, 7, 512])
c1 = tf.layers.batch_normalization(c1, training=is_train)
c1 = tf.maximum(0.2*c1, c1)
# Input is 7x7x512
c2 = tf.layers.conv2d_transpose(c1, 256, 5, [2,2], activation=None, padding='same')
c2 = tf.layers.batch_normalization(c2, training=is_train)
c2 = tf.maximum(0.2*c2, c2)
# Input is 14x14x256
c3 = tf.layers.conv2d_transpose(c2, 128, 5, [2,2], activation=None, padding='same')
c3 = tf.layers.batch_normalization(c3, training=is_train)
c3 = tf.maximum(0.2*c3, c3)
# Convert to 28x28x256 to 28x28xout_channel_dim
logits = tf.layers.conv2d_transpose(c3, out_channel_dim, 5, [1,1], activation=None, padding='same')
# Discriminator excepts -1 to 1 -> apply tanh -> -0.5 to 0.5 since images are scaled to this range.
output = 0.5*tf.tanh(logits)
return output
"""
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"""
tests.test_generator(generator, tf)
Tests Passed
Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:
discriminator(images, reuse=False)generator(z, out_channel_dim, is_train=True)def model_loss(input_real, input_z, out_channel_dim):
"""
Get the loss for the discriminator and generator
:param input_real: Images from the real dataset
:param input_z: Z input
:param out_channel_dim: The number of channels in the output image
:return: A tuple of (discriminator loss, generator loss)
"""
g_model = generator(input_z, out_channel_dim)
d_model_real, d_logits_real = discriminator(input_real)
d_model_fake, d_logits_fake = discriminator(g_model, True)
d_real_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))
d_fake_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
g_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
d_loss = d_real_loss + d_fake_loss
return d_loss, g_loss
"""
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"""
tests.test_model_loss(model_loss)
Tests Passed
Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).
def model_opt(d_loss, g_loss, learning_rate, beta1):
"""
Get optimization operations
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param learning_rate: Learning Rate Placeholder
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
"""
# Get weights and bias to update.
t_vars = tf.trainable_variables()
d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
g_vars = [var for var in t_vars if var.name.startswith('generator')]
# Optimize
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
d_train_opt = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
g_train_opt = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
return d_train_opt, g_train_opt
"""
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"""
tests.test_model_opt(model_opt, tf)
Tests Passed
"""
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"""
import numpy as np
def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
"""
Show example output for the generator
:param sess: TensorFlow session
:param n_images: Number of Images to display
:param input_z: Input Z Tensor
:param out_channel_dim: The number of channels in the output image
:param image_mode: The mode to use for images ("RGB" or "L")
"""
cmap = None if image_mode == 'RGB' else 'gray'
z_dim = input_z.get_shape().as_list()[-1]
example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
samples = sess.run(
generator(input_z, out_channel_dim, False),
feed_dict={input_z: example_z})
images_grid = helper.images_square_grid(samples, image_mode)
pyplot.imshow(images_grid, cmap=cmap)
pyplot.show()
Implement train to build and train the GANs. Use the following functions you implemented:
model_inputs(image_width, image_height, image_channels, z_dim)model_loss(input_real, input_z, out_channel_dim)model_opt(d_loss, g_loss, learning_rate, beta1)Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode, freq_see_loss = 10):
"""
Train the GAN
:param epoch_count: Number of epochs
:param batch_size: Batch Size
:param z_dim: Z dimension
:param learning_rate: Learning Rate
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:param get_batches: Function to get batches
:param data_shape: Shape of the data
:param data_image_mode: The image mode to use for images ("RGB" or "L")
"""
# Get the out_channel_dim.
if data_image_mode == "RGB":
out_channel_dim = 3
elif data_image_mode == "L":
out_channel_dim = 1
# Get the image dimensions.
image_width = data_shape[-2]
image_height = data_shape[-3]
# Get the model inputs.
real_input, z_data, learning_rate_placeholder = model_inputs(image_width, image_height, out_channel_dim, z_dim)
# Obtain the losses.
d_loss, g_loss = model_loss(real_input, z_data, out_channel_dim)
# Obtain the model optimization operations.
d_train_opt, g_train_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
# Container to hold the losses.
g_losses = []
d_losses = []
steps = 0
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch_i in range(epoch_count):
for batch_images in get_batches(batch_size):
steps+=1
# Generate the random z vector.
# z_data_input = tf.random_uniform([batch_size, z_dim], -1, 1)
z_data_input = np.random.uniform(-1, 1, size=(batch_size, z_dim))
sess.run(d_train_opt, feed_dict={real_input:batch_images, z_data:z_data_input})
sess.run(g_train_opt, feed_dict={real_input:batch_images, z_data:z_data_input})
if steps % freq_see_loss == 0:
d_loss_out, g_loss_out = sess.run([d_loss, g_loss], feed_dict={real_input:batch_images, z_data:z_data_input})
print('Epoch: {} -- Step: {} -- d_loss: {} -- g_loss: {}'.format(epoch_i, steps, d_loss_out, g_loss_out))
g_losses.append(g_loss_out)
d_losses.append(d_loss_out)
if steps % 100 == 0:
z_data_input_tensor = tf.random_uniform([16, z_dim], -1, 1)
show_generator_output(sess, 16, z_data_input_tensor, out_channel_dim, data_image_mode)
pyplot.figure()
pyplot.plot(g_losses)
pyplot.title('g_losses')
pyplot.figure()
pyplot.plot(d_losses)
pyplot.title('d_losses')
Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.
batch_size = 128
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5
"""
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"""
epochs = 2
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
mnist_dataset.shape, mnist_dataset.image_mode)
Epoch: 0 -- Step: 10 -- d_loss: 0.2919052541255951 -- g_loss: 4.940899848937988 Epoch: 0 -- Step: 20 -- d_loss: 0.022066690027713776 -- g_loss: 4.52243185043335 Epoch: 0 -- Step: 30 -- d_loss: 0.02281767688691616 -- g_loss: 6.639409065246582 Epoch: 0 -- Step: 40 -- d_loss: 0.030390817672014236 -- g_loss: 8.16735553741455 Epoch: 0 -- Step: 50 -- d_loss: 4.331295967102051 -- g_loss: 10.53857135772705 Epoch: 0 -- Step: 60 -- d_loss: 0.7680845260620117 -- g_loss: 1.0419775247573853 Epoch: 0 -- Step: 70 -- d_loss: 0.2806212604045868 -- g_loss: 2.7464795112609863 Epoch: 0 -- Step: 80 -- d_loss: 0.594424843788147 -- g_loss: 1.356933832168579 Epoch: 0 -- Step: 90 -- d_loss: 0.7611536979675293 -- g_loss: 1.7591285705566406 Epoch: 0 -- Step: 100 -- d_loss: 1.1949225664138794 -- g_loss: 0.6835594177246094
Epoch: 0 -- Step: 110 -- d_loss: 1.532126784324646 -- g_loss: 0.47429269552230835 Epoch: 0 -- Step: 120 -- d_loss: 0.9302685260772705 -- g_loss: 1.0733888149261475 Epoch: 0 -- Step: 130 -- d_loss: 1.155641794204712 -- g_loss: 0.9005465507507324 Epoch: 0 -- Step: 140 -- d_loss: 1.834296703338623 -- g_loss: 0.2523607909679413 Epoch: 0 -- Step: 150 -- d_loss: 1.1264755725860596 -- g_loss: 0.6817317008972168 Epoch: 0 -- Step: 160 -- d_loss: 1.051430344581604 -- g_loss: 0.8035173416137695 Epoch: 0 -- Step: 170 -- d_loss: 1.0306580066680908 -- g_loss: 0.7705690860748291 Epoch: 0 -- Step: 180 -- d_loss: 1.166183590888977 -- g_loss: 1.5168412923812866 Epoch: 0 -- Step: 190 -- d_loss: 1.1633694171905518 -- g_loss: 0.9853212833404541 Epoch: 0 -- Step: 200 -- d_loss: 1.1337440013885498 -- g_loss: 0.6978400945663452
Epoch: 0 -- Step: 210 -- d_loss: 1.0198578834533691 -- g_loss: 1.161963701248169 Epoch: 0 -- Step: 220 -- d_loss: 1.0460155010223389 -- g_loss: 0.9255943894386292 Epoch: 0 -- Step: 230 -- d_loss: 1.0392814874649048 -- g_loss: 1.3703691959381104 Epoch: 0 -- Step: 240 -- d_loss: 1.197930932044983 -- g_loss: 0.5464293956756592 Epoch: 0 -- Step: 250 -- d_loss: 0.9769629240036011 -- g_loss: 1.4929358959197998 Epoch: 0 -- Step: 260 -- d_loss: 0.9208825826644897 -- g_loss: 1.2941476106643677 Epoch: 0 -- Step: 270 -- d_loss: 0.9873843193054199 -- g_loss: 1.5691750049591064 Epoch: 0 -- Step: 280 -- d_loss: 0.9561924934387207 -- g_loss: 1.158470869064331 Epoch: 0 -- Step: 290 -- d_loss: 1.0848631858825684 -- g_loss: 1.8248838186264038 Epoch: 0 -- Step: 300 -- d_loss: 0.9550783634185791 -- g_loss: 1.5365937948226929
Epoch: 0 -- Step: 310 -- d_loss: 0.9725456833839417 -- g_loss: 0.7777073383331299 Epoch: 0 -- Step: 320 -- d_loss: 1.6753404140472412 -- g_loss: 2.4892797470092773 Epoch: 0 -- Step: 330 -- d_loss: 1.0453178882598877 -- g_loss: 1.0394611358642578 Epoch: 0 -- Step: 340 -- d_loss: 1.359913945198059 -- g_loss: 0.39264750480651855 Epoch: 0 -- Step: 350 -- d_loss: 0.9763960242271423 -- g_loss: 0.9105708599090576 Epoch: 0 -- Step: 360 -- d_loss: 1.1472727060317993 -- g_loss: 0.5380191802978516 Epoch: 0 -- Step: 370 -- d_loss: 0.983116090297699 -- g_loss: 1.0143802165985107 Epoch: 0 -- Step: 380 -- d_loss: 1.1879642009735107 -- g_loss: 0.5547511577606201 Epoch: 0 -- Step: 390 -- d_loss: 0.9911061525344849 -- g_loss: 1.1106277704238892 Epoch: 0 -- Step: 400 -- d_loss: 1.033574104309082 -- g_loss: 1.387387990951538
Epoch: 0 -- Step: 410 -- d_loss: 0.9704635143280029 -- g_loss: 0.8986825942993164 Epoch: 0 -- Step: 420 -- d_loss: 1.0224109888076782 -- g_loss: 1.1413966417312622 Epoch: 0 -- Step: 430 -- d_loss: 0.94695645570755 -- g_loss: 1.0982029438018799 Epoch: 0 -- Step: 440 -- d_loss: 0.890794575214386 -- g_loss: 1.3059253692626953 Epoch: 0 -- Step: 450 -- d_loss: 1.0167725086212158 -- g_loss: 0.8158342838287354 Epoch: 0 -- Step: 460 -- d_loss: 0.9179112911224365 -- g_loss: 1.2322192192077637 Epoch: 1 -- Step: 470 -- d_loss: 1.1071112155914307 -- g_loss: 0.5699755549430847 Epoch: 1 -- Step: 480 -- d_loss: 1.009651780128479 -- g_loss: 1.1748476028442383 Epoch: 1 -- Step: 490 -- d_loss: 0.98896324634552 -- g_loss: 0.7256466150283813 Epoch: 1 -- Step: 500 -- d_loss: 1.0049916505813599 -- g_loss: 0.7055234909057617
Epoch: 1 -- Step: 510 -- d_loss: 0.9607968926429749 -- g_loss: 1.5129117965698242 Epoch: 1 -- Step: 520 -- d_loss: 0.9008792638778687 -- g_loss: 1.2569299936294556 Epoch: 1 -- Step: 530 -- d_loss: 1.0684170722961426 -- g_loss: 1.382836103439331 Epoch: 1 -- Step: 540 -- d_loss: 0.9576581716537476 -- g_loss: 1.3073469400405884 Epoch: 1 -- Step: 550 -- d_loss: 0.9453575611114502 -- g_loss: 1.049583077430725 Epoch: 1 -- Step: 560 -- d_loss: 1.0407055616378784 -- g_loss: 0.7289315462112427 Epoch: 1 -- Step: 570 -- d_loss: 0.9388839602470398 -- g_loss: 1.0104011297225952 Epoch: 1 -- Step: 580 -- d_loss: 1.0753720998764038 -- g_loss: 1.5687161684036255 Epoch: 1 -- Step: 590 -- d_loss: 0.9824951887130737 -- g_loss: 1.000942349433899 Epoch: 1 -- Step: 600 -- d_loss: 1.0155689716339111 -- g_loss: 0.8422719836235046
Epoch: 1 -- Step: 610 -- d_loss: 1.1190448999404907 -- g_loss: 1.241908311843872 Epoch: 1 -- Step: 620 -- d_loss: 1.0187878608703613 -- g_loss: 1.0736923217773438 Epoch: 1 -- Step: 630 -- d_loss: 1.017331600189209 -- g_loss: 0.7904312610626221 Epoch: 1 -- Step: 640 -- d_loss: 1.2177494764328003 -- g_loss: 0.5454082489013672 Epoch: 1 -- Step: 650 -- d_loss: 1.0041112899780273 -- g_loss: 0.7869515419006348 Epoch: 1 -- Step: 660 -- d_loss: 1.0074472427368164 -- g_loss: 0.7443562150001526 Epoch: 1 -- Step: 670 -- d_loss: 0.9385982751846313 -- g_loss: 1.08158278465271 Epoch: 1 -- Step: 680 -- d_loss: 0.9918556809425354 -- g_loss: 1.1653330326080322 Epoch: 1 -- Step: 690 -- d_loss: 0.9793219566345215 -- g_loss: 0.7843822836875916 Epoch: 1 -- Step: 700 -- d_loss: 1.4795786142349243 -- g_loss: 0.34337317943573
Epoch: 1 -- Step: 710 -- d_loss: 0.93310546875 -- g_loss: 1.6393282413482666 Epoch: 1 -- Step: 720 -- d_loss: 0.8216481804847717 -- g_loss: 1.1438655853271484 Epoch: 1 -- Step: 730 -- d_loss: 0.8867435455322266 -- g_loss: 1.4032829999923706 Epoch: 1 -- Step: 740 -- d_loss: 0.8842298984527588 -- g_loss: 1.3297260999679565 Epoch: 1 -- Step: 750 -- d_loss: 0.9585742354393005 -- g_loss: 0.7389783263206482 Epoch: 1 -- Step: 760 -- d_loss: 0.8262853622436523 -- g_loss: 1.0515427589416504 Epoch: 1 -- Step: 770 -- d_loss: 1.0015227794647217 -- g_loss: 1.4153945446014404 Epoch: 1 -- Step: 780 -- d_loss: 0.9721543788909912 -- g_loss: 0.8989022970199585 Epoch: 1 -- Step: 790 -- d_loss: 0.875064492225647 -- g_loss: 0.9502137899398804 Epoch: 1 -- Step: 800 -- d_loss: 0.8562411665916443 -- g_loss: 0.9120610356330872
Epoch: 1 -- Step: 810 -- d_loss: 0.9958223700523376 -- g_loss: 0.6282020211219788 Epoch: 1 -- Step: 820 -- d_loss: 0.9233318567276001 -- g_loss: 0.8647503852844238 Epoch: 1 -- Step: 830 -- d_loss: 1.004600167274475 -- g_loss: 2.060114622116089 Epoch: 1 -- Step: 840 -- d_loss: 0.9357266426086426 -- g_loss: 0.8173389434814453 Epoch: 1 -- Step: 850 -- d_loss: 0.8042709231376648 -- g_loss: 1.0028992891311646 Epoch: 1 -- Step: 860 -- d_loss: 0.9073479771614075 -- g_loss: 0.9639356136322021 Epoch: 1 -- Step: 870 -- d_loss: 0.8810655474662781 -- g_loss: 0.8622294664382935 Epoch: 1 -- Step: 880 -- d_loss: 0.8318590521812439 -- g_loss: 1.4579449892044067 Epoch: 1 -- Step: 890 -- d_loss: 0.9475924968719482 -- g_loss: 0.7071715593338013 Epoch: 1 -- Step: 900 -- d_loss: 1.0195789337158203 -- g_loss: 0.6908923983573914
Epoch: 1 -- Step: 910 -- d_loss: 0.8795006275177002 -- g_loss: 0.8145804405212402 Epoch: 1 -- Step: 920 -- d_loss: 1.038091778755188 -- g_loss: 0.6284948587417603 Epoch: 1 -- Step: 930 -- d_loss: 0.7910155653953552 -- g_loss: 1.2550705671310425
Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.
batch_size = 128
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 20
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode)
Epoch: 0 -- Step: 10 -- d_loss: 0.6189572811126709 -- g_loss: 1.6682405471801758 Epoch: 0 -- Step: 20 -- d_loss: 0.8532211780548096 -- g_loss: 2.508634567260742 Epoch: 0 -- Step: 30 -- d_loss: 0.9677637219429016 -- g_loss: 2.884580135345459 Epoch: 0 -- Step: 40 -- d_loss: 1.280186414718628 -- g_loss: 0.5198928117752075 Epoch: 0 -- Step: 50 -- d_loss: 1.1457328796386719 -- g_loss: 0.5005475878715515 Epoch: 0 -- Step: 60 -- d_loss: 0.6648433804512024 -- g_loss: 2.4754750728607178 Epoch: 0 -- Step: 70 -- d_loss: 0.4169224202632904 -- g_loss: 1.8632996082305908 Epoch: 0 -- Step: 80 -- d_loss: 0.9443435072898865 -- g_loss: 0.7916808724403381 Epoch: 0 -- Step: 90 -- d_loss: 0.47528398036956787 -- g_loss: 1.4359049797058105 Epoch: 0 -- Step: 100 -- d_loss: 1.1192724704742432 -- g_loss: 0.7107819318771362
Epoch: 0 -- Step: 110 -- d_loss: 0.9428191781044006 -- g_loss: 0.7804093360900879 Epoch: 0 -- Step: 120 -- d_loss: 0.28318312764167786 -- g_loss: 2.026604175567627 Epoch: 0 -- Step: 130 -- d_loss: 0.3575264513492584 -- g_loss: 2.76959228515625 Epoch: 0 -- Step: 140 -- d_loss: 0.8777046203613281 -- g_loss: 0.7794727087020874 Epoch: 0 -- Step: 150 -- d_loss: 0.22578366100788116 -- g_loss: 3.62062406539917 Epoch: 0 -- Step: 160 -- d_loss: 0.13888703286647797 -- g_loss: 4.594459056854248 Epoch: 0 -- Step: 170 -- d_loss: 0.3556753396987915 -- g_loss: 1.8537588119506836 Epoch: 0 -- Step: 180 -- d_loss: 0.21600377559661865 -- g_loss: 2.78483247756958 Epoch: 0 -- Step: 190 -- d_loss: 0.7178511619567871 -- g_loss: 1.0621424913406372 Epoch: 0 -- Step: 200 -- d_loss: 1.7409213781356812 -- g_loss: 0.3326241374015808
Epoch: 0 -- Step: 210 -- d_loss: 1.1162914037704468 -- g_loss: 0.6142660975456238 Epoch: 0 -- Step: 220 -- d_loss: 1.187780737876892 -- g_loss: 0.5684333443641663 Epoch: 0 -- Step: 230 -- d_loss: 0.31298547983169556 -- g_loss: 2.8665857315063477 Epoch: 0 -- Step: 240 -- d_loss: 0.3061736226081848 -- g_loss: 4.465298652648926 Epoch: 0 -- Step: 250 -- d_loss: 0.5903671979904175 -- g_loss: 1.8480724096298218 Epoch: 0 -- Step: 260 -- d_loss: 0.46069276332855225 -- g_loss: 2.3626768589019775 Epoch: 0 -- Step: 270 -- d_loss: 1.2914705276489258 -- g_loss: 1.207122802734375 Epoch: 0 -- Step: 280 -- d_loss: 0.40697237849235535 -- g_loss: 2.079935312271118 Epoch: 0 -- Step: 290 -- d_loss: 0.7015174627304077 -- g_loss: 0.9863153696060181 Epoch: 0 -- Step: 300 -- d_loss: 3.0196330547332764 -- g_loss: 4.714893341064453
Epoch: 0 -- Step: 310 -- d_loss: 0.684442937374115 -- g_loss: 1.6667400598526 Epoch: 0 -- Step: 320 -- d_loss: 1.2834985256195068 -- g_loss: 0.3918673098087311 Epoch: 0 -- Step: 330 -- d_loss: 1.095014214515686 -- g_loss: 0.5890215635299683 Epoch: 0 -- Step: 340 -- d_loss: 1.3010108470916748 -- g_loss: 3.9024429321289062 Epoch: 0 -- Step: 350 -- d_loss: 1.0750625133514404 -- g_loss: 1.6504697799682617 Epoch: 0 -- Step: 360 -- d_loss: 0.8633740544319153 -- g_loss: 1.1450884342193604 Epoch: 0 -- Step: 370 -- d_loss: 0.655682384967804 -- g_loss: 1.1551790237426758 Epoch: 0 -- Step: 380 -- d_loss: 0.32378149032592773 -- g_loss: 1.840850830078125 Epoch: 0 -- Step: 390 -- d_loss: 0.5401840209960938 -- g_loss: 2.5258617401123047 Epoch: 0 -- Step: 400 -- d_loss: 1.4231135845184326 -- g_loss: 1.0094537734985352
Epoch: 0 -- Step: 410 -- d_loss: 1.5186538696289062 -- g_loss: 0.3039957284927368 Epoch: 0 -- Step: 420 -- d_loss: 0.8933823704719543 -- g_loss: 1.017946481704712 Epoch: 0 -- Step: 430 -- d_loss: 0.5030015707015991 -- g_loss: 1.7307782173156738 Epoch: 0 -- Step: 440 -- d_loss: 0.6974966526031494 -- g_loss: 1.0915714502334595 Epoch: 0 -- Step: 450 -- d_loss: 1.3992705345153809 -- g_loss: 2.4568943977355957 Epoch: 0 -- Step: 460 -- d_loss: 1.432362675666809 -- g_loss: 0.3547859191894531 Epoch: 0 -- Step: 470 -- d_loss: 0.3885296881198883 -- g_loss: 3.3084805011749268 Epoch: 0 -- Step: 480 -- d_loss: 1.4424132108688354 -- g_loss: 3.7159488201141357 Epoch: 0 -- Step: 490 -- d_loss: 0.6583214998245239 -- g_loss: 1.8128461837768555 Epoch: 0 -- Step: 500 -- d_loss: 1.0541586875915527 -- g_loss: 1.4473506212234497
Epoch: 0 -- Step: 510 -- d_loss: 0.7510514259338379 -- g_loss: 2.025989294052124 Epoch: 0 -- Step: 520 -- d_loss: 0.4676828980445862 -- g_loss: 1.976776123046875 Epoch: 0 -- Step: 530 -- d_loss: 0.7727198600769043 -- g_loss: 1.4416558742523193 Epoch: 0 -- Step: 540 -- d_loss: 1.3622641563415527 -- g_loss: 1.1495091915130615 Epoch: 0 -- Step: 550 -- d_loss: 0.8533591032028198 -- g_loss: 1.0452699661254883 Epoch: 0 -- Step: 560 -- d_loss: 0.4185512661933899 -- g_loss: 2.6592907905578613 Epoch: 0 -- Step: 570 -- d_loss: 0.41667357087135315 -- g_loss: 1.8484811782836914 Epoch: 0 -- Step: 580 -- d_loss: 1.1975208520889282 -- g_loss: 1.084951400756836 Epoch: 0 -- Step: 590 -- d_loss: 0.40823841094970703 -- g_loss: 1.647853970527649 Epoch: 0 -- Step: 600 -- d_loss: 1.4130827188491821 -- g_loss: 0.6910707354545593
Epoch: 0 -- Step: 610 -- d_loss: 0.39678677916526794 -- g_loss: 2.560805320739746 Epoch: 0 -- Step: 620 -- d_loss: 0.6609561443328857 -- g_loss: 1.0886285305023193 Epoch: 0 -- Step: 630 -- d_loss: 1.5914608240127563 -- g_loss: 3.239980936050415 Epoch: 0 -- Step: 640 -- d_loss: 0.9681727886199951 -- g_loss: 1.6076397895812988 Epoch: 0 -- Step: 650 -- d_loss: 0.7274972200393677 -- g_loss: 2.520704507827759 Epoch: 0 -- Step: 660 -- d_loss: 0.8910841941833496 -- g_loss: 0.8940232992172241 Epoch: 0 -- Step: 670 -- d_loss: 0.4553184509277344 -- g_loss: 1.965212106704712 Epoch: 0 -- Step: 680 -- d_loss: 0.6510272026062012 -- g_loss: 1.2685779333114624 Epoch: 0 -- Step: 690 -- d_loss: 0.8435059785842896 -- g_loss: 2.0915842056274414 Epoch: 0 -- Step: 700 -- d_loss: 0.8578300476074219 -- g_loss: 0.823190450668335
Epoch: 0 -- Step: 710 -- d_loss: 0.4227743446826935 -- g_loss: 2.2841506004333496 Epoch: 0 -- Step: 720 -- d_loss: 0.39046141505241394 -- g_loss: 3.2438244819641113 Epoch: 0 -- Step: 730 -- d_loss: 1.5941988229751587 -- g_loss: 4.497476577758789 Epoch: 0 -- Step: 740 -- d_loss: 0.7174882888793945 -- g_loss: 1.9809353351593018 Epoch: 0 -- Step: 750 -- d_loss: 0.40130746364593506 -- g_loss: 2.518244743347168 Epoch: 0 -- Step: 760 -- d_loss: 0.633373498916626 -- g_loss: 3.046576976776123 Epoch: 0 -- Step: 770 -- d_loss: 0.9137560129165649 -- g_loss: 1.9444615840911865 Epoch: 0 -- Step: 780 -- d_loss: 0.6419381499290466 -- g_loss: 2.358516216278076 Epoch: 0 -- Step: 790 -- d_loss: 1.4742600917816162 -- g_loss: 0.594638466835022 Epoch: 0 -- Step: 800 -- d_loss: 0.9166625142097473 -- g_loss: 2.0175764560699463
Epoch: 0 -- Step: 810 -- d_loss: 0.7758190035820007 -- g_loss: 0.730849027633667 Epoch: 0 -- Step: 820 -- d_loss: 1.4578440189361572 -- g_loss: 0.3882875442504883 Epoch: 0 -- Step: 830 -- d_loss: 0.44379448890686035 -- g_loss: 2.494309186935425 Epoch: 0 -- Step: 840 -- d_loss: 0.3009606599807739 -- g_loss: 3.6628260612487793 Epoch: 0 -- Step: 850 -- d_loss: 0.5243213772773743 -- g_loss: 1.4126404523849487 Epoch: 0 -- Step: 860 -- d_loss: 1.6131051778793335 -- g_loss: 1.1347616910934448 Epoch: 0 -- Step: 870 -- d_loss: 1.640211582183838 -- g_loss: 0.30656397342681885 Epoch: 0 -- Step: 880 -- d_loss: 1.8730353116989136 -- g_loss: 0.24091392755508423 Epoch: 0 -- Step: 890 -- d_loss: 0.7271528840065002 -- g_loss: 1.2307286262512207 Epoch: 0 -- Step: 900 -- d_loss: 0.45269912481307983 -- g_loss: 2.506349563598633
Epoch: 0 -- Step: 910 -- d_loss: 1.7205414772033691 -- g_loss: 0.3101435899734497 Epoch: 0 -- Step: 920 -- d_loss: 0.778600811958313 -- g_loss: 1.340665340423584 Epoch: 0 -- Step: 930 -- d_loss: 0.416753888130188 -- g_loss: 3.8478164672851562 Epoch: 0 -- Step: 940 -- d_loss: 0.6839836835861206 -- g_loss: 1.671226143836975 Epoch: 0 -- Step: 950 -- d_loss: 0.21385543048381805 -- g_loss: 3.712894916534424 Epoch: 0 -- Step: 960 -- d_loss: 1.444953441619873 -- g_loss: 0.5507619380950928 Epoch: 0 -- Step: 970 -- d_loss: 1.3159905672073364 -- g_loss: 0.5107996463775635 Epoch: 0 -- Step: 980 -- d_loss: 1.3881958723068237 -- g_loss: 0.5218123197555542 Epoch: 0 -- Step: 990 -- d_loss: 1.3753917217254639 -- g_loss: 0.6706399917602539 Epoch: 0 -- Step: 1000 -- d_loss: 1.1528619527816772 -- g_loss: 0.8228816986083984
Epoch: 0 -- Step: 1010 -- d_loss: 0.9613427519798279 -- g_loss: 1.3325039148330688 Epoch: 0 -- Step: 1020 -- d_loss: 0.6985876560211182 -- g_loss: 2.3830606937408447 Epoch: 0 -- Step: 1030 -- d_loss: 1.4630494117736816 -- g_loss: 0.80255126953125 Epoch: 0 -- Step: 1040 -- d_loss: 1.2302714586257935 -- g_loss: 0.933463454246521 Epoch: 0 -- Step: 1050 -- d_loss: 0.9717111587524414 -- g_loss: 1.260368824005127 Epoch: 0 -- Step: 1060 -- d_loss: 0.5876362323760986 -- g_loss: 1.2069242000579834 Epoch: 0 -- Step: 1070 -- d_loss: 1.1813292503356934 -- g_loss: 1.2407647371292114 Epoch: 0 -- Step: 1080 -- d_loss: 0.3050784468650818 -- g_loss: 2.800346851348877 Epoch: 0 -- Step: 1090 -- d_loss: 2.4665019512176514 -- g_loss: 3.7656726837158203 Epoch: 0 -- Step: 1100 -- d_loss: 1.0486547946929932 -- g_loss: 1.5476114749908447
Epoch: 0 -- Step: 1110 -- d_loss: 0.9820250272750854 -- g_loss: 0.8171892166137695 Epoch: 0 -- Step: 1120 -- d_loss: 1.6059978008270264 -- g_loss: 0.3154286742210388 Epoch: 0 -- Step: 1130 -- d_loss: 0.9904441833496094 -- g_loss: 1.22186279296875 Epoch: 0 -- Step: 1140 -- d_loss: 0.2859627604484558 -- g_loss: 2.696101188659668 Epoch: 0 -- Step: 1150 -- d_loss: 1.6777154207229614 -- g_loss: 4.121460437774658 Epoch: 0 -- Step: 1160 -- d_loss: 0.6745917797088623 -- g_loss: 0.8780162334442139 Epoch: 0 -- Step: 1170 -- d_loss: 0.9326121807098389 -- g_loss: 1.2943236827850342 Epoch: 0 -- Step: 1180 -- d_loss: 0.5793116688728333 -- g_loss: 1.0676884651184082 Epoch: 0 -- Step: 1190 -- d_loss: 1.2927860021591187 -- g_loss: 0.6350408792495728 Epoch: 0 -- Step: 1200 -- d_loss: 1.3902781009674072 -- g_loss: 0.6302004456520081
Epoch: 0 -- Step: 1210 -- d_loss: 1.1440271139144897 -- g_loss: 0.7656934261322021 Epoch: 0 -- Step: 1220 -- d_loss: 1.3002328872680664 -- g_loss: 0.715445876121521 Epoch: 0 -- Step: 1230 -- d_loss: 1.2937426567077637 -- g_loss: 0.6865297555923462 Epoch: 0 -- Step: 1240 -- d_loss: 1.5434268712997437 -- g_loss: 0.6973769664764404 Epoch: 0 -- Step: 1250 -- d_loss: 1.4455633163452148 -- g_loss: 0.5808602571487427 Epoch: 0 -- Step: 1260 -- d_loss: 1.0961345434188843 -- g_loss: 0.8604573607444763 Epoch: 0 -- Step: 1270 -- d_loss: 0.9594032168388367 -- g_loss: 1.9071661233901978 Epoch: 0 -- Step: 1280 -- d_loss: 0.9304260015487671 -- g_loss: 1.3109021186828613 Epoch: 0 -- Step: 1290 -- d_loss: 1.1514148712158203 -- g_loss: 0.7132852673530579 Epoch: 0 -- Step: 1300 -- d_loss: 1.1793675422668457 -- g_loss: 1.0359140634536743
Epoch: 0 -- Step: 1310 -- d_loss: 1.0821325778961182 -- g_loss: 1.1243438720703125 Epoch: 0 -- Step: 1320 -- d_loss: 1.0742340087890625 -- g_loss: 1.3470410108566284 Epoch: 0 -- Step: 1330 -- d_loss: 0.42404162883758545 -- g_loss: 1.7010838985443115 Epoch: 0 -- Step: 1340 -- d_loss: 1.1779853105545044 -- g_loss: 2.0520894527435303 Epoch: 0 -- Step: 1350 -- d_loss: 1.0360634326934814 -- g_loss: 0.9936537742614746 Epoch: 0 -- Step: 1360 -- d_loss: 0.9809082746505737 -- g_loss: 0.6320120096206665 Epoch: 0 -- Step: 1370 -- d_loss: 0.8686448335647583 -- g_loss: 1.4107683897018433 Epoch: 0 -- Step: 1380 -- d_loss: 1.162491798400879 -- g_loss: 0.906292200088501 Epoch: 0 -- Step: 1390 -- d_loss: 1.1113702058792114 -- g_loss: 0.891510009765625 Epoch: 0 -- Step: 1400 -- d_loss: 0.2266317754983902 -- g_loss: 3.0489165782928467
Epoch: 0 -- Step: 1410 -- d_loss: 1.035365104675293 -- g_loss: 0.6253972053527832 Epoch: 0 -- Step: 1420 -- d_loss: 0.6521323323249817 -- g_loss: 1.610490322113037 Epoch: 0 -- Step: 1430 -- d_loss: 0.6945587396621704 -- g_loss: 1.284684181213379 Epoch: 0 -- Step: 1440 -- d_loss: 0.39973515272140503 -- g_loss: 2.2424755096435547 Epoch: 0 -- Step: 1450 -- d_loss: 0.9458611011505127 -- g_loss: 0.6484680771827698 Epoch: 0 -- Step: 1460 -- d_loss: 0.22823208570480347 -- g_loss: 2.924443006515503 Epoch: 0 -- Step: 1470 -- d_loss: 0.6245657801628113 -- g_loss: 0.9658270478248596 Epoch: 0 -- Step: 1480 -- d_loss: 0.19764626026153564 -- g_loss: 2.3248441219329834 Epoch: 0 -- Step: 1490 -- d_loss: 1.6478025913238525 -- g_loss: 0.2690458297729492 Epoch: 0 -- Step: 1500 -- d_loss: 0.8623889684677124 -- g_loss: 2.9659221172332764
Epoch: 0 -- Step: 1510 -- d_loss: 1.5176411867141724 -- g_loss: 1.7084048986434937 Epoch: 0 -- Step: 1520 -- d_loss: 1.014229655265808 -- g_loss: 1.354475975036621 Epoch: 0 -- Step: 1530 -- d_loss: 0.15211786329746246 -- g_loss: 4.3755598068237305 Epoch: 0 -- Step: 1540 -- d_loss: 1.0428096055984497 -- g_loss: 0.5147484540939331 Epoch: 0 -- Step: 1550 -- d_loss: 0.36802083253860474 -- g_loss: 1.5795575380325317 Epoch: 0 -- Step: 1560 -- d_loss: 0.46364694833755493 -- g_loss: 1.7814652919769287 Epoch: 0 -- Step: 1570 -- d_loss: 0.921968936920166 -- g_loss: 0.8456337451934814 Epoch: 0 -- Step: 1580 -- d_loss: 1.2094566822052002 -- g_loss: 0.721060574054718 Epoch: 1 -- Step: 1590 -- d_loss: 1.2473468780517578 -- g_loss: 0.7497451305389404 Epoch: 1 -- Step: 1600 -- d_loss: 0.6745474338531494 -- g_loss: 2.7854301929473877
Epoch: 1 -- Step: 1610 -- d_loss: 0.9143364429473877 -- g_loss: 1.1025044918060303 Epoch: 1 -- Step: 1620 -- d_loss: 1.008831262588501 -- g_loss: 0.5783803462982178 Epoch: 1 -- Step: 1630 -- d_loss: 1.249293565750122 -- g_loss: 0.530289888381958 Epoch: 1 -- Step: 1640 -- d_loss: 0.7459416389465332 -- g_loss: 1.271433711051941 Epoch: 1 -- Step: 1650 -- d_loss: 1.446341633796692 -- g_loss: 0.4242762625217438 Epoch: 1 -- Step: 1660 -- d_loss: 0.9493142366409302 -- g_loss: 1.037452220916748 Epoch: 1 -- Step: 1670 -- d_loss: 0.7378976941108704 -- g_loss: 1.6037817001342773 Epoch: 1 -- Step: 1680 -- d_loss: 0.2288271188735962 -- g_loss: 2.5756006240844727 Epoch: 1 -- Step: 1690 -- d_loss: 0.8916651606559753 -- g_loss: 0.7521088123321533 Epoch: 1 -- Step: 1700 -- d_loss: 0.4584102928638458 -- g_loss: 1.3949971199035645
Epoch: 1 -- Step: 1710 -- d_loss: 1.0619703531265259 -- g_loss: 0.5304038524627686 Epoch: 1 -- Step: 1720 -- d_loss: 0.897750735282898 -- g_loss: 2.481067180633545 Epoch: 1 -- Step: 1730 -- d_loss: 0.7138628959655762 -- g_loss: 1.9476619958877563 Epoch: 1 -- Step: 1740 -- d_loss: 0.6286993026733398 -- g_loss: 1.0541020631790161 Epoch: 1 -- Step: 1750 -- d_loss: 0.32747936248779297 -- g_loss: 2.8508076667785645 Epoch: 1 -- Step: 1760 -- d_loss: 1.001863718032837 -- g_loss: 1.3201563358306885 Epoch: 1 -- Step: 1770 -- d_loss: 0.36181312799453735 -- g_loss: 1.5450842380523682 Epoch: 1 -- Step: 1780 -- d_loss: 1.144686222076416 -- g_loss: 3.2428181171417236 Epoch: 1 -- Step: 1790 -- d_loss: 1.490280032157898 -- g_loss: 0.8920574188232422 Epoch: 1 -- Step: 1800 -- d_loss: 0.9011308550834656 -- g_loss: 1.3165229558944702
Epoch: 1 -- Step: 1810 -- d_loss: 0.8414962887763977 -- g_loss: 0.9211695194244385 Epoch: 1 -- Step: 1820 -- d_loss: 1.1024104356765747 -- g_loss: 0.5241304636001587 Epoch: 1 -- Step: 1830 -- d_loss: 0.17971862852573395 -- g_loss: 3.7634243965148926 Epoch: 1 -- Step: 1840 -- d_loss: 0.6849057674407959 -- g_loss: 0.9918780326843262 Epoch: 1 -- Step: 1850 -- d_loss: 0.7813336849212646 -- g_loss: 2.1332669258117676 Epoch: 1 -- Step: 1860 -- d_loss: 0.08805727958679199 -- g_loss: 4.440838813781738 Epoch: 1 -- Step: 1870 -- d_loss: 0.38721761107444763 -- g_loss: 2.508232593536377 Epoch: 1 -- Step: 1880 -- d_loss: 1.4452531337738037 -- g_loss: 0.4298607409000397 Epoch: 1 -- Step: 1890 -- d_loss: 1.0737653970718384 -- g_loss: 0.797720193862915 Epoch: 1 -- Step: 1900 -- d_loss: 1.0560133457183838 -- g_loss: 1.833406925201416
Epoch: 1 -- Step: 1910 -- d_loss: 0.6736504435539246 -- g_loss: 1.4597465991973877 Epoch: 1 -- Step: 1920 -- d_loss: 0.3199922442436218 -- g_loss: 4.544075012207031 Epoch: 1 -- Step: 1930 -- d_loss: 0.4952291250228882 -- g_loss: 3.423349142074585 Epoch: 1 -- Step: 1940 -- d_loss: 1.8775442838668823 -- g_loss: 0.21041536331176758 Epoch: 1 -- Step: 1950 -- d_loss: 0.6771110892295837 -- g_loss: 4.860257148742676 Epoch: 1 -- Step: 1960 -- d_loss: 1.0452930927276611 -- g_loss: 0.7398726344108582 Epoch: 1 -- Step: 1970 -- d_loss: 0.39288127422332764 -- g_loss: 2.7531087398529053 Epoch: 1 -- Step: 1980 -- d_loss: 1.6806256771087646 -- g_loss: 0.2732156217098236 Epoch: 1 -- Step: 1990 -- d_loss: 0.44811874628067017 -- g_loss: 2.763991355895996 Epoch: 1 -- Step: 2000 -- d_loss: 0.8011709451675415 -- g_loss: 1.3325421810150146
Epoch: 1 -- Step: 2010 -- d_loss: 0.19646961987018585 -- g_loss: 4.6606245040893555 Epoch: 1 -- Step: 2020 -- d_loss: 0.22011731564998627 -- g_loss: 2.663156270980835 Epoch: 1 -- Step: 2030 -- d_loss: 0.9595638513565063 -- g_loss: 0.9171736240386963 Epoch: 1 -- Step: 2040 -- d_loss: 0.7857937216758728 -- g_loss: 2.158007860183716 Epoch: 1 -- Step: 2050 -- d_loss: 0.4943925440311432 -- g_loss: 2.0018887519836426 Epoch: 1 -- Step: 2060 -- d_loss: 0.5928472280502319 -- g_loss: 1.2624804973602295 Epoch: 1 -- Step: 2070 -- d_loss: 0.44147390127182007 -- g_loss: 1.359309434890747 Epoch: 1 -- Step: 2080 -- d_loss: 1.0342658758163452 -- g_loss: 0.9056569337844849 Epoch: 1 -- Step: 2090 -- d_loss: 0.7531095743179321 -- g_loss: 0.9352422952651978 Epoch: 1 -- Step: 2100 -- d_loss: 0.5067130327224731 -- g_loss: 1.3402283191680908
Epoch: 1 -- Step: 2110 -- d_loss: 0.24693331122398376 -- g_loss: 2.529682159423828 Epoch: 1 -- Step: 2120 -- d_loss: 0.8291499614715576 -- g_loss: 1.5238170623779297 Epoch: 1 -- Step: 2130 -- d_loss: 0.8627752661705017 -- g_loss: 1.3388694524765015 Epoch: 1 -- Step: 2140 -- d_loss: 1.4138318300247192 -- g_loss: 0.3802984952926636 Epoch: 1 -- Step: 2150 -- d_loss: 0.8594006896018982 -- g_loss: 0.8091235160827637 Epoch: 1 -- Step: 2160 -- d_loss: 1.1150556802749634 -- g_loss: 0.5688972473144531 Epoch: 1 -- Step: 2170 -- d_loss: 0.9080210328102112 -- g_loss: 2.244438648223877 Epoch: 1 -- Step: 2180 -- d_loss: 0.6697580814361572 -- g_loss: 3.86273193359375 Epoch: 1 -- Step: 2190 -- d_loss: 0.3745391368865967 -- g_loss: 3.3579213619232178 Epoch: 1 -- Step: 2200 -- d_loss: 1.018622636795044 -- g_loss: 0.8034167289733887
Epoch: 1 -- Step: 2210 -- d_loss: 0.2299160659313202 -- g_loss: 2.281777858734131 Epoch: 1 -- Step: 2220 -- d_loss: 0.06177879124879837 -- g_loss: 4.220396995544434 Epoch: 1 -- Step: 2230 -- d_loss: 1.1448066234588623 -- g_loss: 3.045391082763672 Epoch: 1 -- Step: 2240 -- d_loss: 1.5430643558502197 -- g_loss: 3.2492504119873047 Epoch: 1 -- Step: 2250 -- d_loss: 0.6009900569915771 -- g_loss: 3.4213218688964844 Epoch: 1 -- Step: 2260 -- d_loss: 0.8504784107208252 -- g_loss: 0.9323097467422485 Epoch: 1 -- Step: 2270 -- d_loss: 0.393809050321579 -- g_loss: 1.748704433441162 Epoch: 1 -- Step: 2280 -- d_loss: 0.25307849049568176 -- g_loss: 4.387582778930664 Epoch: 1 -- Step: 2290 -- d_loss: 1.324120044708252 -- g_loss: 0.43965810537338257 Epoch: 1 -- Step: 2300 -- d_loss: 0.6997941732406616 -- g_loss: 0.9417262077331543
Epoch: 1 -- Step: 2310 -- d_loss: 0.30377572774887085 -- g_loss: 3.654876708984375 Epoch: 1 -- Step: 2320 -- d_loss: 0.8927281498908997 -- g_loss: 1.8831982612609863 Epoch: 1 -- Step: 2330 -- d_loss: 1.1628141403198242 -- g_loss: 0.8577392101287842 Epoch: 1 -- Step: 2340 -- d_loss: 0.754732608795166 -- g_loss: 1.312701940536499 Epoch: 1 -- Step: 2350 -- d_loss: 0.9813492298126221 -- g_loss: 1.9699616432189941 Epoch: 1 -- Step: 2360 -- d_loss: 1.2246966361999512 -- g_loss: 0.553169846534729 Epoch: 1 -- Step: 2370 -- d_loss: 1.2551432847976685 -- g_loss: 0.6487826108932495 Epoch: 1 -- Step: 2380 -- d_loss: 1.2201637029647827 -- g_loss: 0.9191102981567383 Epoch: 1 -- Step: 2390 -- d_loss: 0.9486553072929382 -- g_loss: 0.7537978887557983 Epoch: 1 -- Step: 2400 -- d_loss: 0.7684076428413391 -- g_loss: 0.8908189535140991
Epoch: 1 -- Step: 2410 -- d_loss: 0.48774921894073486 -- g_loss: 2.9584290981292725 Epoch: 1 -- Step: 2420 -- d_loss: 0.7799083590507507 -- g_loss: 0.800193190574646 Epoch: 1 -- Step: 2430 -- d_loss: 1.110849380493164 -- g_loss: 0.5364092588424683 Epoch: 1 -- Step: 2440 -- d_loss: 0.4876430034637451 -- g_loss: 1.4436841011047363 Epoch: 1 -- Step: 2450 -- d_loss: 0.3334721028804779 -- g_loss: 1.6953037977218628 Epoch: 1 -- Step: 2460 -- d_loss: 0.6587044596672058 -- g_loss: 2.2734644412994385 Epoch: 1 -- Step: 2470 -- d_loss: 0.48972392082214355 -- g_loss: 1.3888195753097534 Epoch: 1 -- Step: 2480 -- d_loss: 0.32353705167770386 -- g_loss: 3.1681182384490967 Epoch: 1 -- Step: 2490 -- d_loss: 0.4051210582256317 -- g_loss: 1.86868417263031 Epoch: 1 -- Step: 2500 -- d_loss: 1.0619707107543945 -- g_loss: 1.5270417928695679
Epoch: 1 -- Step: 2510 -- d_loss: 0.7011055946350098 -- g_loss: 1.6834099292755127 Epoch: 1 -- Step: 2520 -- d_loss: 0.5878304243087769 -- g_loss: 1.3226125240325928 Epoch: 1 -- Step: 2530 -- d_loss: 1.3865361213684082 -- g_loss: 1.6932761669158936 Epoch: 1 -- Step: 2540 -- d_loss: 0.6256345510482788 -- g_loss: 0.9829944968223572 Epoch: 1 -- Step: 2550 -- d_loss: 0.6100661754608154 -- g_loss: 1.055193305015564 Epoch: 1 -- Step: 2560 -- d_loss: 0.458606094121933 -- g_loss: 1.6847631931304932 Epoch: 1 -- Step: 2570 -- d_loss: 0.7810660600662231 -- g_loss: 1.1656467914581299 Epoch: 1 -- Step: 2580 -- d_loss: 0.7144728899002075 -- g_loss: 0.9831439256668091 Epoch: 1 -- Step: 2590 -- d_loss: 0.31848299503326416 -- g_loss: 1.8415813446044922 Epoch: 1 -- Step: 2600 -- d_loss: 0.8965672850608826 -- g_loss: 0.8640711307525635
Epoch: 1 -- Step: 2610 -- d_loss: 0.22800302505493164 -- g_loss: 2.1979598999023438 Epoch: 1 -- Step: 2620 -- d_loss: 0.7049496173858643 -- g_loss: 1.4538196325302124 Epoch: 1 -- Step: 2630 -- d_loss: 1.0260095596313477 -- g_loss: 0.8647108674049377 Epoch: 1 -- Step: 2640 -- d_loss: 0.907428503036499 -- g_loss: 1.824440598487854 Epoch: 1 -- Step: 2650 -- d_loss: 0.5123563408851624 -- g_loss: 1.46145498752594 Epoch: 1 -- Step: 2660 -- d_loss: 1.1321978569030762 -- g_loss: 0.504685640335083 Epoch: 1 -- Step: 2670 -- d_loss: 1.281786561012268 -- g_loss: 3.4532551765441895 Epoch: 1 -- Step: 2680 -- d_loss: 1.1051082611083984 -- g_loss: 0.7637423276901245 Epoch: 1 -- Step: 2690 -- d_loss: 0.3421911597251892 -- g_loss: 1.8770090341567993 Epoch: 1 -- Step: 2700 -- d_loss: 0.3198506832122803 -- g_loss: 1.8746377229690552
Epoch: 1 -- Step: 2710 -- d_loss: 1.024990439414978 -- g_loss: 0.6590636968612671 Epoch: 1 -- Step: 2720 -- d_loss: 0.6382807493209839 -- g_loss: 2.271178960800171 Epoch: 1 -- Step: 2730 -- d_loss: 0.41477954387664795 -- g_loss: 1.56165611743927 Epoch: 1 -- Step: 2740 -- d_loss: 0.29212966561317444 -- g_loss: 2.3253159523010254 Epoch: 1 -- Step: 2750 -- d_loss: 0.7294071316719055 -- g_loss: 1.606353998184204 Epoch: 1 -- Step: 2760 -- d_loss: 1.0778355598449707 -- g_loss: 0.5555144548416138 Epoch: 1 -- Step: 2770 -- d_loss: 2.0632822513580322 -- g_loss: 2.805485725402832 Epoch: 1 -- Step: 2780 -- d_loss: 1.3376878499984741 -- g_loss: 2.359102487564087 Epoch: 1 -- Step: 2790 -- d_loss: 0.0784018412232399 -- g_loss: 3.7593154907226562 Epoch: 1 -- Step: 2800 -- d_loss: 0.08340398222208023 -- g_loss: 3.3333678245544434
Epoch: 1 -- Step: 2810 -- d_loss: 0.0893305093050003 -- g_loss: 3.539496421813965 Epoch: 1 -- Step: 2820 -- d_loss: 0.04896511882543564 -- g_loss: 5.1101460456848145 Epoch: 1 -- Step: 2830 -- d_loss: 0.06659230589866638 -- g_loss: 6.093572616577148 Epoch: 1 -- Step: 2840 -- d_loss: 0.0599786750972271 -- g_loss: 7.264162063598633 Epoch: 1 -- Step: 2850 -- d_loss: 0.08693937957286835 -- g_loss: 3.8572816848754883 Epoch: 1 -- Step: 2860 -- d_loss: 0.43569353222846985 -- g_loss: 2.846342086791992 Epoch: 1 -- Step: 2870 -- d_loss: 0.8249791860580444 -- g_loss: 2.034977912902832 Epoch: 1 -- Step: 2880 -- d_loss: 0.9513798952102661 -- g_loss: 0.6730558276176453 Epoch: 1 -- Step: 2890 -- d_loss: 0.8948664665222168 -- g_loss: 2.0704145431518555 Epoch: 1 -- Step: 2900 -- d_loss: 0.9996280670166016 -- g_loss: 1.9473296403884888
Epoch: 1 -- Step: 2910 -- d_loss: 0.7685137987136841 -- g_loss: 0.9961667060852051 Epoch: 1 -- Step: 2920 -- d_loss: 0.8057112693786621 -- g_loss: 0.9706466794013977 Epoch: 1 -- Step: 2930 -- d_loss: 0.41943109035491943 -- g_loss: 2.0261142253875732 Epoch: 1 -- Step: 2940 -- d_loss: 0.4977257549762726 -- g_loss: 2.6526331901550293 Epoch: 1 -- Step: 2950 -- d_loss: 0.49013257026672363 -- g_loss: 1.957890510559082 Epoch: 1 -- Step: 2960 -- d_loss: 0.27289068698883057 -- g_loss: 2.306978702545166 Epoch: 1 -- Step: 2970 -- d_loss: 1.2801964282989502 -- g_loss: 3.275139093399048 Epoch: 1 -- Step: 2980 -- d_loss: 1.0112515687942505 -- g_loss: 1.392381191253662 Epoch: 1 -- Step: 2990 -- d_loss: 0.9109930396080017 -- g_loss: 0.8895823955535889 Epoch: 1 -- Step: 3000 -- d_loss: 1.3237780332565308 -- g_loss: 1.5866823196411133
Epoch: 1 -- Step: 3010 -- d_loss: 0.669760525226593 -- g_loss: 1.2037558555603027 Epoch: 1 -- Step: 3020 -- d_loss: 0.2376224547624588 -- g_loss: 2.6989660263061523 Epoch: 1 -- Step: 3030 -- d_loss: 0.1359354704618454 -- g_loss: 3.5557374954223633 Epoch: 1 -- Step: 3040 -- d_loss: 0.4777992069721222 -- g_loss: 3.1317663192749023 Epoch: 1 -- Step: 3050 -- d_loss: 0.33261367678642273 -- g_loss: 1.6987736225128174 Epoch: 1 -- Step: 3060 -- d_loss: 1.0918747186660767 -- g_loss: 0.7531015872955322 Epoch: 1 -- Step: 3070 -- d_loss: 1.2153334617614746 -- g_loss: 0.498384028673172 Epoch: 1 -- Step: 3080 -- d_loss: 0.9932056665420532 -- g_loss: 1.8824739456176758 Epoch: 1 -- Step: 3090 -- d_loss: 0.7682011127471924 -- g_loss: 0.8555682897567749 Epoch: 1 -- Step: 3100 -- d_loss: 0.7925916910171509 -- g_loss: 0.7977929711341858
Epoch: 1 -- Step: 3110 -- d_loss: 1.2969719171524048 -- g_loss: 0.5853017568588257 Epoch: 1 -- Step: 3120 -- d_loss: 0.31166112422943115 -- g_loss: 3.407209873199463 Epoch: 1 -- Step: 3130 -- d_loss: 1.2169276475906372 -- g_loss: 2.703270435333252 Epoch: 1 -- Step: 3140 -- d_loss: 0.42582935094833374 -- g_loss: 1.6600674390792847 Epoch: 1 -- Step: 3150 -- d_loss: 1.001047968864441 -- g_loss: 4.625617027282715 Epoch: 1 -- Step: 3160 -- d_loss: 0.6985657811164856 -- g_loss: 1.1908767223358154 Epoch: 2 -- Step: 3170 -- d_loss: 0.29009851813316345 -- g_loss: 1.6615567207336426 Epoch: 2 -- Step: 3180 -- d_loss: 0.8661779165267944 -- g_loss: 1.003366231918335 Epoch: 2 -- Step: 3190 -- d_loss: 1.4076929092407227 -- g_loss: 0.36670640110969543 Epoch: 2 -- Step: 3200 -- d_loss: 0.7694706916809082 -- g_loss: 1.5796113014221191
Epoch: 2 -- Step: 3210 -- d_loss: 0.9568800926208496 -- g_loss: 0.7181556224822998 Epoch: 2 -- Step: 3220 -- d_loss: 0.5805347561836243 -- g_loss: 2.9342613220214844 Epoch: 2 -- Step: 3230 -- d_loss: 1.1499100923538208 -- g_loss: 0.5152454376220703 Epoch: 2 -- Step: 3240 -- d_loss: 0.8806171417236328 -- g_loss: 0.696605384349823 Epoch: 2 -- Step: 3250 -- d_loss: 0.6265655159950256 -- g_loss: 1.2017940282821655 Epoch: 2 -- Step: 3260 -- d_loss: 0.24059928953647614 -- g_loss: 2.4839000701904297 Epoch: 2 -- Step: 3270 -- d_loss: 1.6888768672943115 -- g_loss: 0.2793145179748535 Epoch: 2 -- Step: 3280 -- d_loss: 0.6288878917694092 -- g_loss: 1.2352522611618042 Epoch: 2 -- Step: 3290 -- d_loss: 0.7239007949829102 -- g_loss: 0.9694056510925293 Epoch: 2 -- Step: 3300 -- d_loss: 0.6743775606155396 -- g_loss: 1.0168302059173584
Epoch: 2 -- Step: 3310 -- d_loss: 0.6556815505027771 -- g_loss: 1.21886146068573 Epoch: 2 -- Step: 3320 -- d_loss: 0.35170120000839233 -- g_loss: 1.7813106775283813 Epoch: 2 -- Step: 3330 -- d_loss: 1.3128255605697632 -- g_loss: 4.337895393371582 Epoch: 2 -- Step: 3340 -- d_loss: 0.6073940992355347 -- g_loss: 1.1648606061935425 Epoch: 2 -- Step: 3350 -- d_loss: 1.118332862854004 -- g_loss: 0.9040751457214355 Epoch: 2 -- Step: 3360 -- d_loss: 0.8949957489967346 -- g_loss: 0.8830150961875916 Epoch: 2 -- Step: 3370 -- d_loss: 1.20716392993927 -- g_loss: 0.5028070211410522 Epoch: 2 -- Step: 3380 -- d_loss: 0.935355544090271 -- g_loss: 1.1674938201904297 Epoch: 2 -- Step: 3390 -- d_loss: 0.28273874521255493 -- g_loss: 2.2288124561309814 Epoch: 2 -- Step: 3400 -- d_loss: 0.7660622000694275 -- g_loss: 0.8368101119995117
Epoch: 2 -- Step: 3410 -- d_loss: 0.5753461122512817 -- g_loss: 1.753221035003662 Epoch: 2 -- Step: 3420 -- d_loss: 0.6892865896224976 -- g_loss: 1.7191226482391357 Epoch: 2 -- Step: 3430 -- d_loss: 0.49666470289230347 -- g_loss: 1.7402688264846802 Epoch: 2 -- Step: 3440 -- d_loss: 0.8028000593185425 -- g_loss: 0.905215859413147 Epoch: 2 -- Step: 3450 -- d_loss: 0.6236992478370667 -- g_loss: 1.9435522556304932 Epoch: 2 -- Step: 3460 -- d_loss: 0.845778226852417 -- g_loss: 0.8505269289016724 Epoch: 2 -- Step: 3470 -- d_loss: 1.1955647468566895 -- g_loss: 0.48256486654281616 Epoch: 2 -- Step: 3480 -- d_loss: 0.7675691843032837 -- g_loss: 3.0199828147888184 Epoch: 2 -- Step: 3490 -- d_loss: 1.2858165502548218 -- g_loss: 0.43922045826911926 Epoch: 2 -- Step: 3500 -- d_loss: 0.4433920979499817 -- g_loss: 1.4608986377716064
Epoch: 2 -- Step: 3510 -- d_loss: 1.3709290027618408 -- g_loss: 0.43468835949897766 Epoch: 2 -- Step: 3520 -- d_loss: 1.7289843559265137 -- g_loss: 0.34178704023361206 Epoch: 2 -- Step: 3530 -- d_loss: 0.7364873290061951 -- g_loss: 1.3518478870391846 Epoch: 2 -- Step: 3540 -- d_loss: 0.6546793580055237 -- g_loss: 2.0470175743103027 Epoch: 2 -- Step: 3550 -- d_loss: 0.9462769031524658 -- g_loss: 0.6735161542892456 Epoch: 2 -- Step: 3560 -- d_loss: 0.5579084753990173 -- g_loss: 1.0388672351837158 Epoch: 2 -- Step: 3570 -- d_loss: 0.5177963376045227 -- g_loss: 1.6111059188842773 Epoch: 2 -- Step: 3580 -- d_loss: 0.3274531364440918 -- g_loss: 2.1800382137298584 Epoch: 2 -- Step: 3590 -- d_loss: 0.13334988057613373 -- g_loss: 4.694622993469238 Epoch: 2 -- Step: 3600 -- d_loss: 1.6623793840408325 -- g_loss: 0.2918531894683838
Epoch: 2 -- Step: 3610 -- d_loss: 0.40280890464782715 -- g_loss: 6.692235946655273 Epoch: 2 -- Step: 3620 -- d_loss: 0.4522182047367096 -- g_loss: 4.735857009887695 Epoch: 2 -- Step: 3630 -- d_loss: 1.522251009941101 -- g_loss: 0.36408013105392456 Epoch: 2 -- Step: 3640 -- d_loss: 0.8382296562194824 -- g_loss: 0.6921917796134949 Epoch: 2 -- Step: 3650 -- d_loss: 0.18171411752700806 -- g_loss: 2.695725440979004 Epoch: 2 -- Step: 3660 -- d_loss: 0.06802873313426971 -- g_loss: 3.842641592025757 Epoch: 2 -- Step: 3670 -- d_loss: 0.11877075582742691 -- g_loss: 3.999785900115967 Epoch: 2 -- Step: 3680 -- d_loss: 0.04153033345937729 -- g_loss: 5.3943281173706055 Epoch: 2 -- Step: 3690 -- d_loss: 0.31957340240478516 -- g_loss: 3.117780923843384 Epoch: 2 -- Step: 3700 -- d_loss: 0.3839847445487976 -- g_loss: 1.5587834119796753
Epoch: 2 -- Step: 3710 -- d_loss: 0.6384445428848267 -- g_loss: 1.012465000152588 Epoch: 2 -- Step: 3720 -- d_loss: 0.7533572316169739 -- g_loss: 2.0536561012268066 Epoch: 2 -- Step: 3730 -- d_loss: 0.9288071393966675 -- g_loss: 0.88169926404953 Epoch: 2 -- Step: 3740 -- d_loss: 0.2540970742702484 -- g_loss: 4.4203596115112305 Epoch: 2 -- Step: 3750 -- d_loss: 0.4611859619617462 -- g_loss: 1.568291187286377 Epoch: 2 -- Step: 3760 -- d_loss: 1.6333048343658447 -- g_loss: 1.6580734252929688 Epoch: 2 -- Step: 3770 -- d_loss: 0.2702997624874115 -- g_loss: 2.546860933303833 Epoch: 2 -- Step: 3780 -- d_loss: 0.8142828941345215 -- g_loss: 1.0768111944198608 Epoch: 2 -- Step: 3790 -- d_loss: 1.324234127998352 -- g_loss: 0.4772186875343323 Epoch: 2 -- Step: 3800 -- d_loss: 1.4370650053024292 -- g_loss: 1.1019949913024902
Epoch: 2 -- Step: 3810 -- d_loss: 0.5463930368423462 -- g_loss: 1.9869844913482666 Epoch: 2 -- Step: 3820 -- d_loss: 1.2772639989852905 -- g_loss: 0.4486595392227173 Epoch: 2 -- Step: 3830 -- d_loss: 0.7804673314094543 -- g_loss: 1.312464714050293 Epoch: 2 -- Step: 3840 -- d_loss: 0.576621413230896 -- g_loss: 1.217700481414795 Epoch: 2 -- Step: 3850 -- d_loss: 0.4114431142807007 -- g_loss: 1.6361477375030518 Epoch: 2 -- Step: 3860 -- d_loss: 0.4751374125480652 -- g_loss: 1.7017781734466553 Epoch: 2 -- Step: 3870 -- d_loss: 0.5663338303565979 -- g_loss: 2.2473535537719727 Epoch: 2 -- Step: 3880 -- d_loss: 1.1705561876296997 -- g_loss: 1.146543025970459 Epoch: 2 -- Step: 3890 -- d_loss: 1.6891741752624512 -- g_loss: 2.239342212677002 Epoch: 2 -- Step: 3900 -- d_loss: 0.8769132494926453 -- g_loss: 0.821391224861145
Epoch: 2 -- Step: 3910 -- d_loss: 1.1362590789794922 -- g_loss: 1.4404550790786743 Epoch: 2 -- Step: 3920 -- d_loss: 1.0052272081375122 -- g_loss: 1.340287446975708 Epoch: 2 -- Step: 3930 -- d_loss: 0.6612470149993896 -- g_loss: 1.423740267753601 Epoch: 2 -- Step: 3940 -- d_loss: 1.0631006956100464 -- g_loss: 0.729343056678772 Epoch: 2 -- Step: 3950 -- d_loss: 0.6537075638771057 -- g_loss: 2.4307703971862793 Epoch: 2 -- Step: 3960 -- d_loss: 1.092867136001587 -- g_loss: 0.5515989661216736 Epoch: 2 -- Step: 3970 -- d_loss: 1.0143593549728394 -- g_loss: 0.9416922926902771 Epoch: 2 -- Step: 3980 -- d_loss: 0.5799768567085266 -- g_loss: 1.2054327726364136 Epoch: 2 -- Step: 3990 -- d_loss: 1.065787672996521 -- g_loss: 0.7175549864768982 Epoch: 2 -- Step: 4000 -- d_loss: 1.0736268758773804 -- g_loss: 0.9380308985710144
Epoch: 2 -- Step: 4010 -- d_loss: 0.49524644017219543 -- g_loss: 1.6278610229492188 Epoch: 2 -- Step: 4020 -- d_loss: 0.5700353980064392 -- g_loss: 3.399916410446167 Epoch: 2 -- Step: 4030 -- d_loss: 0.29479190707206726 -- g_loss: 2.682537078857422 Epoch: 2 -- Step: 4040 -- d_loss: 1.5328028202056885 -- g_loss: 0.3403250277042389 Epoch: 2 -- Step: 4050 -- d_loss: 1.1912624835968018 -- g_loss: 0.9502177834510803 Epoch: 2 -- Step: 4060 -- d_loss: 1.4540774822235107 -- g_loss: 0.37589454650878906 Epoch: 2 -- Step: 4070 -- d_loss: 0.7142375707626343 -- g_loss: 2.816758632659912 Epoch: 2 -- Step: 4080 -- d_loss: 0.6742832660675049 -- g_loss: 1.7923628091812134 Epoch: 2 -- Step: 4090 -- d_loss: 1.131827473640442 -- g_loss: 0.5962620377540588 Epoch: 2 -- Step: 4100 -- d_loss: 0.7776491641998291 -- g_loss: 1.5221519470214844
Epoch: 2 -- Step: 4110 -- d_loss: 1.296432375907898 -- g_loss: 1.7925742864608765 Epoch: 2 -- Step: 4120 -- d_loss: 0.9120518565177917 -- g_loss: 1.806172490119934 Epoch: 2 -- Step: 4130 -- d_loss: 0.8554316163063049 -- g_loss: 1.6359946727752686 Epoch: 2 -- Step: 4140 -- d_loss: 0.3466651439666748 -- g_loss: 2.4593915939331055 Epoch: 2 -- Step: 4150 -- d_loss: 1.902287483215332 -- g_loss: 2.714090347290039 Epoch: 2 -- Step: 4160 -- d_loss: 0.08307395130395889 -- g_loss: 7.267695426940918 Epoch: 2 -- Step: 4170 -- d_loss: 0.9030163884162903 -- g_loss: 0.7578636407852173 Epoch: 2 -- Step: 4180 -- d_loss: 1.286759853363037 -- g_loss: 0.42272430658340454 Epoch: 2 -- Step: 4190 -- d_loss: 1.203334093093872 -- g_loss: 0.5179607272148132 Epoch: 2 -- Step: 4200 -- d_loss: 0.9875295758247375 -- g_loss: 0.6481342315673828
Epoch: 2 -- Step: 4210 -- d_loss: 1.289412260055542 -- g_loss: 0.5204260349273682 Epoch: 2 -- Step: 4220 -- d_loss: 0.9167395830154419 -- g_loss: 1.954056739807129 Epoch: 2 -- Step: 4230 -- d_loss: 1.3024401664733887 -- g_loss: 0.46617376804351807 Epoch: 2 -- Step: 4240 -- d_loss: 0.8536108136177063 -- g_loss: 0.7725452780723572 Epoch: 2 -- Step: 4250 -- d_loss: 0.7554200887680054 -- g_loss: 1.42948317527771 Epoch: 2 -- Step: 4260 -- d_loss: 1.2968056201934814 -- g_loss: 0.9223647117614746 Epoch: 2 -- Step: 4270 -- d_loss: 1.5667248964309692 -- g_loss: 0.4492717683315277 Epoch: 2 -- Step: 4280 -- d_loss: 0.8849894404411316 -- g_loss: 1.1901586055755615 Epoch: 2 -- Step: 4290 -- d_loss: 1.1160900592803955 -- g_loss: 0.892753541469574 Epoch: 2 -- Step: 4300 -- d_loss: 0.602445125579834 -- g_loss: 1.9186862707138062
Epoch: 2 -- Step: 4310 -- d_loss: 1.1005351543426514 -- g_loss: 1.2975653409957886 Epoch: 2 -- Step: 4320 -- d_loss: 1.4000720977783203 -- g_loss: 0.791260838508606 Epoch: 2 -- Step: 4330 -- d_loss: 1.0205223560333252 -- g_loss: 0.6119193434715271 Epoch: 2 -- Step: 4340 -- d_loss: 1.0044894218444824 -- g_loss: 0.7549805641174316 Epoch: 2 -- Step: 4350 -- d_loss: 0.7490428686141968 -- g_loss: 1.8998996019363403 Epoch: 2 -- Step: 4360 -- d_loss: 0.9470696449279785 -- g_loss: 0.9477285742759705 Epoch: 2 -- Step: 4370 -- d_loss: 1.0221588611602783 -- g_loss: 0.7816911935806274 Epoch: 2 -- Step: 4380 -- d_loss: 0.7640642523765564 -- g_loss: 1.2674691677093506 Epoch: 2 -- Step: 4390 -- d_loss: 1.0103931427001953 -- g_loss: 1.1449129581451416 Epoch: 2 -- Step: 4400 -- d_loss: 0.8146729469299316 -- g_loss: 1.723823070526123
Epoch: 2 -- Step: 4410 -- d_loss: 0.797262966632843 -- g_loss: 1.0392987728118896 Epoch: 2 -- Step: 4420 -- d_loss: 0.956682026386261 -- g_loss: 0.8275312185287476 Epoch: 2 -- Step: 4430 -- d_loss: 0.6455158591270447 -- g_loss: 1.1909425258636475 Epoch: 2 -- Step: 4440 -- d_loss: 1.172816514968872 -- g_loss: 0.7637572884559631 Epoch: 2 -- Step: 4450 -- d_loss: 0.8283816576004028 -- g_loss: 1.0262383222579956 Epoch: 2 -- Step: 4460 -- d_loss: 0.9379554390907288 -- g_loss: 0.9616621732711792 Epoch: 2 -- Step: 4470 -- d_loss: 0.9448939561843872 -- g_loss: 1.5062098503112793 Epoch: 2 -- Step: 4480 -- d_loss: 1.158558964729309 -- g_loss: 2.4880781173706055 Epoch: 2 -- Step: 4490 -- d_loss: 1.210967779159546 -- g_loss: 0.5548526644706726 Epoch: 2 -- Step: 4500 -- d_loss: 0.26027634739875793 -- g_loss: 4.278383731842041
Epoch: 2 -- Step: 4510 -- d_loss: 0.5499380826950073 -- g_loss: 1.4243980646133423 Epoch: 2 -- Step: 4520 -- d_loss: 0.6024597883224487 -- g_loss: 5.156578540802002 Epoch: 2 -- Step: 4530 -- d_loss: 0.9747189283370972 -- g_loss: 0.7755573987960815 Epoch: 2 -- Step: 4540 -- d_loss: 0.6433472633361816 -- g_loss: 2.24735426902771 Epoch: 2 -- Step: 4550 -- d_loss: 1.3030067682266235 -- g_loss: 0.45421555638313293 Epoch: 2 -- Step: 4560 -- d_loss: 1.190370798110962 -- g_loss: 0.6806381344795227 Epoch: 2 -- Step: 4570 -- d_loss: 0.40899771451950073 -- g_loss: 1.9386446475982666 Epoch: 2 -- Step: 4580 -- d_loss: 1.3571817874908447 -- g_loss: 0.4291446805000305 Epoch: 2 -- Step: 4590 -- d_loss: 0.32681572437286377 -- g_loss: 2.1676254272460938 Epoch: 2 -- Step: 4600 -- d_loss: 0.9201475977897644 -- g_loss: 0.9527219533920288
Epoch: 2 -- Step: 4610 -- d_loss: 0.7532637119293213 -- g_loss: 1.1414250135421753 Epoch: 2 -- Step: 4620 -- d_loss: 1.0481843948364258 -- g_loss: 0.8851184844970703 Epoch: 2 -- Step: 4630 -- d_loss: 1.1702464818954468 -- g_loss: 0.7170353531837463 Epoch: 2 -- Step: 4640 -- d_loss: 1.0761473178863525 -- g_loss: 0.8203911781311035 Epoch: 2 -- Step: 4650 -- d_loss: 1.1074144840240479 -- g_loss: 0.5884042978286743 Epoch: 2 -- Step: 4660 -- d_loss: 1.0406407117843628 -- g_loss: 0.8615257740020752 Epoch: 2 -- Step: 4670 -- d_loss: 1.002254605293274 -- g_loss: 0.6978989839553833 Epoch: 2 -- Step: 4680 -- d_loss: 1.0568811893463135 -- g_loss: 0.8664877414703369 Epoch: 2 -- Step: 4690 -- d_loss: 0.8699911236763 -- g_loss: 1.4517979621887207 Epoch: 2 -- Step: 4700 -- d_loss: 0.9394158124923706 -- g_loss: 0.8424553871154785
Epoch: 2 -- Step: 4710 -- d_loss: 1.002976417541504 -- g_loss: 0.7617977261543274 Epoch: 2 -- Step: 4720 -- d_loss: 1.3814973831176758 -- g_loss: 0.4685993790626526 Epoch: 2 -- Step: 4730 -- d_loss: 0.7285329103469849 -- g_loss: 1.1691986322402954 Epoch: 2 -- Step: 4740 -- d_loss: 0.9048235416412354 -- g_loss: 0.7851202487945557 Epoch: 3 -- Step: 4750 -- d_loss: 0.8240299820899963 -- g_loss: 0.9800426363945007 Epoch: 3 -- Step: 4760 -- d_loss: 1.175516128540039 -- g_loss: 0.5262134075164795 Epoch: 3 -- Step: 4770 -- d_loss: 0.9847347736358643 -- g_loss: 1.231579303741455 Epoch: 3 -- Step: 4780 -- d_loss: 1.1977438926696777 -- g_loss: 0.7252588272094727 Epoch: 3 -- Step: 4790 -- d_loss: 1.034786343574524 -- g_loss: 0.6686787605285645 Epoch: 3 -- Step: 4800 -- d_loss: 1.0495612621307373 -- g_loss: 1.0435450077056885
Epoch: 3 -- Step: 4810 -- d_loss: 1.0089670419692993 -- g_loss: 0.7771851420402527 Epoch: 3 -- Step: 4820 -- d_loss: 0.7196758985519409 -- g_loss: 1.9954019784927368 Epoch: 3 -- Step: 4830 -- d_loss: 1.3503437042236328 -- g_loss: 2.3564045429229736 Epoch: 3 -- Step: 4840 -- d_loss: 0.8164265751838684 -- g_loss: 0.7325164079666138 Epoch: 3 -- Step: 4850 -- d_loss: 0.9229680299758911 -- g_loss: 0.8851931095123291 Epoch: 3 -- Step: 4860 -- d_loss: 0.4089655876159668 -- g_loss: 2.7609596252441406 Epoch: 3 -- Step: 4870 -- d_loss: 1.101258635520935 -- g_loss: 0.5967549085617065 Epoch: 3 -- Step: 4880 -- d_loss: 1.252939224243164 -- g_loss: 0.5346750020980835 Epoch: 3 -- Step: 4890 -- d_loss: 0.9879447817802429 -- g_loss: 1.0637470483779907 Epoch: 3 -- Step: 4900 -- d_loss: 0.9881429672241211 -- g_loss: 0.9730182886123657
Epoch: 3 -- Step: 4910 -- d_loss: 1.323712944984436 -- g_loss: 0.47481364011764526 Epoch: 3 -- Step: 4920 -- d_loss: 1.254725694656372 -- g_loss: 0.7404286861419678 Epoch: 3 -- Step: 4930 -- d_loss: 0.9356290102005005 -- g_loss: 1.044600248336792 Epoch: 3 -- Step: 4940 -- d_loss: 1.3893543481826782 -- g_loss: 1.7570455074310303 Epoch: 3 -- Step: 4950 -- d_loss: 0.7158617973327637 -- g_loss: 1.0356918573379517 Epoch: 3 -- Step: 4960 -- d_loss: 1.1422092914581299 -- g_loss: 0.6765161752700806 Epoch: 3 -- Step: 4970 -- d_loss: 1.5222032070159912 -- g_loss: 0.3330411911010742 Epoch: 3 -- Step: 4980 -- d_loss: 0.9931207895278931 -- g_loss: 0.7070862054824829 Epoch: 3 -- Step: 4990 -- d_loss: 0.9255528450012207 -- g_loss: 1.1679362058639526 Epoch: 3 -- Step: 5000 -- d_loss: 1.0575222969055176 -- g_loss: 0.701143741607666
Epoch: 3 -- Step: 5010 -- d_loss: 0.8465750217437744 -- g_loss: 0.9840108156204224 Epoch: 3 -- Step: 5020 -- d_loss: 0.9356163740158081 -- g_loss: 0.7169027328491211 Epoch: 3 -- Step: 5030 -- d_loss: 1.2666337490081787 -- g_loss: 0.46268826723098755 Epoch: 3 -- Step: 5040 -- d_loss: 0.8951082825660706 -- g_loss: 0.7147142291069031 Epoch: 3 -- Step: 5050 -- d_loss: 0.6891434192657471 -- g_loss: 1.5255241394042969 Epoch: 3 -- Step: 5060 -- d_loss: 1.9152950048446655 -- g_loss: 0.22472533583641052 Epoch: 3 -- Step: 5070 -- d_loss: 0.905669093132019 -- g_loss: 0.7989577054977417 Epoch: 3 -- Step: 5080 -- d_loss: 2.153576374053955 -- g_loss: 2.0948665142059326 Epoch: 3 -- Step: 5090 -- d_loss: 1.3003357648849487 -- g_loss: 0.8477039337158203 Epoch: 3 -- Step: 5100 -- d_loss: 1.0931402444839478 -- g_loss: 0.7266299724578857
Epoch: 3 -- Step: 5110 -- d_loss: 0.3306162655353546 -- g_loss: 3.915597438812256 Epoch: 3 -- Step: 5120 -- d_loss: 1.2048698663711548 -- g_loss: 0.48473942279815674 Epoch: 3 -- Step: 5130 -- d_loss: 0.45854753255844116 -- g_loss: 2.159616470336914 Epoch: 3 -- Step: 5140 -- d_loss: 0.535489559173584 -- g_loss: 1.4881104230880737 Epoch: 3 -- Step: 5150 -- d_loss: 1.394092321395874 -- g_loss: 0.39434757828712463 Epoch: 3 -- Step: 5160 -- d_loss: 0.4155859351158142 -- g_loss: 2.5711827278137207 Epoch: 3 -- Step: 5170 -- d_loss: 1.1925657987594604 -- g_loss: 0.5772310495376587 Epoch: 3 -- Step: 5180 -- d_loss: 0.9950437545776367 -- g_loss: 0.7712680101394653 Epoch: 3 -- Step: 5190 -- d_loss: 1.0699068307876587 -- g_loss: 0.7254975438117981 Epoch: 3 -- Step: 5200 -- d_loss: 0.8853362798690796 -- g_loss: 1.0651769638061523
Epoch: 3 -- Step: 5210 -- d_loss: 0.38721758127212524 -- g_loss: 2.23933744430542 Epoch: 3 -- Step: 5220 -- d_loss: 1.5050864219665527 -- g_loss: 0.33433717489242554 Epoch: 3 -- Step: 5230 -- d_loss: 0.6753207445144653 -- g_loss: 1.5848042964935303 Epoch: 3 -- Step: 5240 -- d_loss: 0.6335067749023438 -- g_loss: 1.2648394107818604 Epoch: 3 -- Step: 5250 -- d_loss: 1.5463643074035645 -- g_loss: 0.3061347007751465 Epoch: 3 -- Step: 5260 -- d_loss: 1.0519863367080688 -- g_loss: 0.6431902050971985 Epoch: 3 -- Step: 5270 -- d_loss: 1.1261370182037354 -- g_loss: 0.5507720708847046 Epoch: 3 -- Step: 5280 -- d_loss: 1.195791244506836 -- g_loss: 0.49632689356803894 Epoch: 3 -- Step: 5290 -- d_loss: 0.9410687685012817 -- g_loss: 1.474910020828247 Epoch: 3 -- Step: 5300 -- d_loss: 1.216943383216858 -- g_loss: 0.5124677419662476
Epoch: 3 -- Step: 5310 -- d_loss: 0.47983768582344055 -- g_loss: 2.77785062789917 Epoch: 3 -- Step: 5320 -- d_loss: 1.4223164319992065 -- g_loss: 0.4083544611930847 Epoch: 3 -- Step: 5330 -- d_loss: 1.205836534500122 -- g_loss: 0.7588340044021606 Epoch: 3 -- Step: 5340 -- d_loss: 0.33407995104789734 -- g_loss: 2.261955738067627 Epoch: 3 -- Step: 5350 -- d_loss: 1.0522369146347046 -- g_loss: 0.6875384449958801 Epoch: 3 -- Step: 5360 -- d_loss: 0.46150171756744385 -- g_loss: 2.317592144012451 Epoch: 3 -- Step: 5370 -- d_loss: 1.932619571685791 -- g_loss: 2.6070380210876465 Epoch: 3 -- Step: 5380 -- d_loss: 1.283249855041504 -- g_loss: 0.47476181387901306 Epoch: 3 -- Step: 5390 -- d_loss: 1.2117061614990234 -- g_loss: 0.5282039642333984 Epoch: 3 -- Step: 5400 -- d_loss: 0.8087238073348999 -- g_loss: 1.075230360031128
Epoch: 3 -- Step: 5410 -- d_loss: 1.163163423538208 -- g_loss: 1.254831075668335 Epoch: 3 -- Step: 5420 -- d_loss: 1.7766035795211792 -- g_loss: 2.4976248741149902 Epoch: 3 -- Step: 5430 -- d_loss: 0.5746219158172607 -- g_loss: 1.4071643352508545 Epoch: 3 -- Step: 5440 -- d_loss: 1.2612725496292114 -- g_loss: 0.5770018696784973 Epoch: 3 -- Step: 5450 -- d_loss: 1.022613525390625 -- g_loss: 0.7680991291999817 Epoch: 3 -- Step: 5460 -- d_loss: 1.1207033395767212 -- g_loss: 0.6751740574836731 Epoch: 3 -- Step: 5470 -- d_loss: 1.2164629697799683 -- g_loss: 0.5284708142280579 Epoch: 3 -- Step: 5480 -- d_loss: 0.7736544013023376 -- g_loss: 1.432891607284546 Epoch: 3 -- Step: 5490 -- d_loss: 0.5073819160461426 -- g_loss: 1.3797824382781982 Epoch: 3 -- Step: 5500 -- d_loss: 0.6508986949920654 -- g_loss: 1.979662537574768
Epoch: 3 -- Step: 5510 -- d_loss: 1.2963377237319946 -- g_loss: 0.6999146938323975 Epoch: 3 -- Step: 5520 -- d_loss: 1.0844650268554688 -- g_loss: 1.354755163192749 Epoch: 3 -- Step: 5530 -- d_loss: 1.1404368877410889 -- g_loss: 0.6195816993713379 Epoch: 3 -- Step: 5540 -- d_loss: 1.5668585300445557 -- g_loss: 2.4167017936706543 Epoch: 3 -- Step: 5550 -- d_loss: 0.8507634401321411 -- g_loss: 0.9086087942123413 Epoch: 3 -- Step: 5560 -- d_loss: 0.8652869462966919 -- g_loss: 1.6014868021011353 Epoch: 3 -- Step: 5570 -- d_loss: 0.9826459884643555 -- g_loss: 0.9910964965820312 Epoch: 3 -- Step: 5580 -- d_loss: 1.49909508228302 -- g_loss: 0.3357955813407898 Epoch: 3 -- Step: 5590 -- d_loss: 1.0451278686523438 -- g_loss: 0.7956720590591431 Epoch: 3 -- Step: 5600 -- d_loss: 0.6449445486068726 -- g_loss: 1.2732644081115723
Epoch: 3 -- Step: 5610 -- d_loss: 1.3974528312683105 -- g_loss: 0.39043712615966797 Epoch: 3 -- Step: 5620 -- d_loss: 0.9441946744918823 -- g_loss: 1.092693567276001 Epoch: 3 -- Step: 5630 -- d_loss: 0.9428244233131409 -- g_loss: 0.9428096413612366 Epoch: 3 -- Step: 5640 -- d_loss: 0.8812214136123657 -- g_loss: 0.9653010368347168 Epoch: 3 -- Step: 5650 -- d_loss: 0.8037968873977661 -- g_loss: 1.0642467737197876 Epoch: 3 -- Step: 5660 -- d_loss: 1.0012836456298828 -- g_loss: 1.1109740734100342 Epoch: 3 -- Step: 5670 -- d_loss: 1.103225827217102 -- g_loss: 0.6877787709236145 Epoch: 3 -- Step: 5680 -- d_loss: 0.6385979652404785 -- g_loss: 1.1690053939819336 Epoch: 3 -- Step: 5690 -- d_loss: 1.0260324478149414 -- g_loss: 2.400099277496338 Epoch: 3 -- Step: 5700 -- d_loss: 1.1723442077636719 -- g_loss: 1.3603415489196777
Epoch: 3 -- Step: 5710 -- d_loss: 1.308438777923584 -- g_loss: 0.6660190224647522 Epoch: 3 -- Step: 5720 -- d_loss: 0.8001521229743958 -- g_loss: 1.0508111715316772 Epoch: 3 -- Step: 5730 -- d_loss: 0.596301257610321 -- g_loss: 1.5377311706542969 Epoch: 3 -- Step: 5740 -- d_loss: 1.0913294553756714 -- g_loss: 0.9279686212539673 Epoch: 3 -- Step: 5750 -- d_loss: 0.9960941076278687 -- g_loss: 1.6694600582122803 Epoch: 3 -- Step: 5760 -- d_loss: 0.6924658417701721 -- g_loss: 1.934288501739502 Epoch: 3 -- Step: 5770 -- d_loss: 0.6997553110122681 -- g_loss: 1.2715425491333008 Epoch: 3 -- Step: 5780 -- d_loss: 1.2305903434753418 -- g_loss: 1.2116847038269043 Epoch: 3 -- Step: 5790 -- d_loss: 0.9889053106307983 -- g_loss: 1.4000513553619385 Epoch: 3 -- Step: 5800 -- d_loss: 0.9928570985794067 -- g_loss: 1.3051717281341553
Epoch: 3 -- Step: 5810 -- d_loss: 1.181612253189087 -- g_loss: 1.1756232976913452 Epoch: 3 -- Step: 5820 -- d_loss: 1.0290725231170654 -- g_loss: 1.3010485172271729 Epoch: 3 -- Step: 5830 -- d_loss: 1.054758906364441 -- g_loss: 0.8248740434646606 Epoch: 3 -- Step: 5840 -- d_loss: 0.6248085498809814 -- g_loss: 1.0652040243148804 Epoch: 3 -- Step: 5850 -- d_loss: 1.6900285482406616 -- g_loss: 0.28188198804855347 Epoch: 3 -- Step: 5860 -- d_loss: 1.1439521312713623 -- g_loss: 1.6338670253753662 Epoch: 3 -- Step: 5870 -- d_loss: 1.220057487487793 -- g_loss: 0.5152255892753601 Epoch: 3 -- Step: 5880 -- d_loss: 1.1211826801300049 -- g_loss: 1.581239938735962 Epoch: 3 -- Step: 5890 -- d_loss: 1.5361520051956177 -- g_loss: 0.3290800154209137 Epoch: 3 -- Step: 5900 -- d_loss: 0.9475340843200684 -- g_loss: 0.8995022773742676
Epoch: 3 -- Step: 5910 -- d_loss: 0.987594723701477 -- g_loss: 0.7717670202255249 Epoch: 3 -- Step: 5920 -- d_loss: 1.3477517366409302 -- g_loss: 0.4304496645927429 Epoch: 3 -- Step: 5930 -- d_loss: 1.0007368326187134 -- g_loss: 1.0014270544052124 Epoch: 3 -- Step: 5940 -- d_loss: 1.2800774574279785 -- g_loss: 0.6205130815505981 Epoch: 3 -- Step: 5950 -- d_loss: 0.6304811239242554 -- g_loss: 1.7546133995056152 Epoch: 3 -- Step: 5960 -- d_loss: 0.7225198745727539 -- g_loss: 1.3087315559387207 Epoch: 3 -- Step: 5970 -- d_loss: 0.37634268403053284 -- g_loss: 3.9841160774230957 Epoch: 3 -- Step: 5980 -- d_loss: 0.3474220633506775 -- g_loss: 1.6820133924484253 Epoch: 3 -- Step: 5990 -- d_loss: 1.0591250658035278 -- g_loss: 0.5508956909179688 Epoch: 3 -- Step: 6000 -- d_loss: 0.29962158203125 -- g_loss: 2.491875648498535
Epoch: 3 -- Step: 6010 -- d_loss: 0.5620837807655334 -- g_loss: 1.641538143157959 Epoch: 3 -- Step: 6020 -- d_loss: 1.1683261394500732 -- g_loss: 0.529352068901062 Epoch: 3 -- Step: 6030 -- d_loss: 1.1865274906158447 -- g_loss: 0.6815805435180664 Epoch: 3 -- Step: 6040 -- d_loss: 1.1640112400054932 -- g_loss: 1.0597326755523682 Epoch: 3 -- Step: 6050 -- d_loss: 1.861109972000122 -- g_loss: 3.2512502670288086 Epoch: 3 -- Step: 6060 -- d_loss: 2.398837089538574 -- g_loss: 2.5196309089660645 Epoch: 3 -- Step: 6070 -- d_loss: 1.3611853122711182 -- g_loss: 0.8159546852111816 Epoch: 3 -- Step: 6080 -- d_loss: 1.1365385055541992 -- g_loss: 0.708412766456604 Epoch: 3 -- Step: 6090 -- d_loss: 1.3540856838226318 -- g_loss: 0.5300081372261047 Epoch: 3 -- Step: 6100 -- d_loss: 0.3131251335144043 -- g_loss: 3.15681791305542
Epoch: 3 -- Step: 6110 -- d_loss: 0.7792664170265198 -- g_loss: 1.0164389610290527 Epoch: 3 -- Step: 6120 -- d_loss: 1.1833986043930054 -- g_loss: 0.493785560131073 Epoch: 3 -- Step: 6130 -- d_loss: 1.0727019309997559 -- g_loss: 0.6580410003662109 Epoch: 3 -- Step: 6140 -- d_loss: 1.8746691942214966 -- g_loss: 0.21639952063560486 Epoch: 3 -- Step: 6150 -- d_loss: 1.2793294191360474 -- g_loss: 0.564586877822876 Epoch: 3 -- Step: 6160 -- d_loss: 1.1175563335418701 -- g_loss: 0.6367015242576599 Epoch: 3 -- Step: 6170 -- d_loss: 1.7195631265640259 -- g_loss: 0.27366286516189575 Epoch: 3 -- Step: 6180 -- d_loss: 1.0432626008987427 -- g_loss: 0.8812515735626221 Epoch: 3 -- Step: 6190 -- d_loss: 1.094472050666809 -- g_loss: 0.6175071597099304 Epoch: 3 -- Step: 6200 -- d_loss: 1.5531095266342163 -- g_loss: 0.33468765020370483
Epoch: 3 -- Step: 6210 -- d_loss: 0.817779541015625 -- g_loss: 1.134703516960144 Epoch: 3 -- Step: 6220 -- d_loss: 0.9523108005523682 -- g_loss: 1.269548773765564 Epoch: 3 -- Step: 6230 -- d_loss: 1.2665038108825684 -- g_loss: 0.5797390937805176 Epoch: 3 -- Step: 6240 -- d_loss: 1.2864046096801758 -- g_loss: 0.4772995710372925 Epoch: 3 -- Step: 6250 -- d_loss: 1.374794602394104 -- g_loss: 0.49750879406929016 Epoch: 3 -- Step: 6260 -- d_loss: 1.0339537858963013 -- g_loss: 0.7902674674987793 Epoch: 3 -- Step: 6270 -- d_loss: 1.35903799533844 -- g_loss: 0.41644877195358276 Epoch: 3 -- Step: 6280 -- d_loss: 1.1293046474456787 -- g_loss: 0.6605925559997559 Epoch: 3 -- Step: 6290 -- d_loss: 1.1547636985778809 -- g_loss: 2.328793525695801 Epoch: 3 -- Step: 6300 -- d_loss: 1.1299505233764648 -- g_loss: 0.5374144315719604
Epoch: 3 -- Step: 6310 -- d_loss: 0.5068479776382446 -- g_loss: 1.520826816558838 Epoch: 3 -- Step: 6320 -- d_loss: 1.5659478902816772 -- g_loss: 0.30547478795051575 Epoch: 4 -- Step: 6330 -- d_loss: 1.1507902145385742 -- g_loss: 0.8064966797828674 Epoch: 4 -- Step: 6340 -- d_loss: 1.7655692100524902 -- g_loss: 0.3084558844566345 Epoch: 4 -- Step: 6350 -- d_loss: 1.0405657291412354 -- g_loss: 1.8480496406555176 Epoch: 4 -- Step: 6360 -- d_loss: 0.6984837055206299 -- g_loss: 1.9873138666152954 Epoch: 4 -- Step: 6370 -- d_loss: 1.379252552986145 -- g_loss: 0.38198959827423096 Epoch: 4 -- Step: 6380 -- d_loss: 0.7334651947021484 -- g_loss: 1.1100900173187256 Epoch: 4 -- Step: 6390 -- d_loss: 0.824618935585022 -- g_loss: 0.9816322326660156 Epoch: 4 -- Step: 6400 -- d_loss: 1.1599431037902832 -- g_loss: 0.6826377511024475
Epoch: 4 -- Step: 6410 -- d_loss: 1.19020414352417 -- g_loss: 1.1428515911102295 Epoch: 4 -- Step: 6420 -- d_loss: 1.2823481559753418 -- g_loss: 0.9397339820861816 Epoch: 4 -- Step: 6430 -- d_loss: 1.668558120727539 -- g_loss: 0.2896915376186371 Epoch: 4 -- Step: 6440 -- d_loss: 1.1529167890548706 -- g_loss: 0.8236013054847717 Epoch: 4 -- Step: 6450 -- d_loss: 1.1308892965316772 -- g_loss: 1.2427459955215454 Epoch: 4 -- Step: 6460 -- d_loss: 0.9845600724220276 -- g_loss: 0.9431906938552856 Epoch: 4 -- Step: 6470 -- d_loss: 1.4573497772216797 -- g_loss: 0.3776877224445343 Epoch: 4 -- Step: 6480 -- d_loss: 1.0525286197662354 -- g_loss: 1.268998146057129 Epoch: 4 -- Step: 6490 -- d_loss: 1.1587775945663452 -- g_loss: 0.5428244471549988 Epoch: 4 -- Step: 6500 -- d_loss: 1.0930140018463135 -- g_loss: 1.2268633842468262
Epoch: 4 -- Step: 6510 -- d_loss: 1.528927206993103 -- g_loss: 0.3179084062576294 Epoch: 4 -- Step: 6520 -- d_loss: 1.053758978843689 -- g_loss: 0.776018500328064 Epoch: 4 -- Step: 6530 -- d_loss: 1.5615414381027222 -- g_loss: 0.3314703106880188 Epoch: 4 -- Step: 6540 -- d_loss: 1.0432255268096924 -- g_loss: 0.8143783211708069 Epoch: 4 -- Step: 6550 -- d_loss: 1.1087183952331543 -- g_loss: 0.9568529725074768 Epoch: 4 -- Step: 6560 -- d_loss: 1.1164079904556274 -- g_loss: 0.8750157356262207 Epoch: 4 -- Step: 6570 -- d_loss: 1.0096138715744019 -- g_loss: 2.150491714477539 Epoch: 4 -- Step: 6580 -- d_loss: 1.052868127822876 -- g_loss: 1.4616501331329346 Epoch: 4 -- Step: 6590 -- d_loss: 0.91474848985672 -- g_loss: 0.9869033098220825 Epoch: 4 -- Step: 6600 -- d_loss: 1.0102543830871582 -- g_loss: 1.743870735168457
Epoch: 4 -- Step: 6610 -- d_loss: 1.1144073009490967 -- g_loss: 0.6447474956512451 Epoch: 4 -- Step: 6620 -- d_loss: 0.8759568929672241 -- g_loss: 1.3015265464782715 Epoch: 4 -- Step: 6630 -- d_loss: 1.5949208736419678 -- g_loss: 0.324055016040802 Epoch: 4 -- Step: 6640 -- d_loss: 1.1661083698272705 -- g_loss: 0.7961821556091309 Epoch: 4 -- Step: 6650 -- d_loss: 1.1754796504974365 -- g_loss: 1.4253476858139038 Epoch: 4 -- Step: 6660 -- d_loss: 0.6433048248291016 -- g_loss: 1.4644243717193604 Epoch: 4 -- Step: 6670 -- d_loss: 1.2989585399627686 -- g_loss: 0.5608990788459778 Epoch: 4 -- Step: 6680 -- d_loss: 1.2074685096740723 -- g_loss: 1.454014539718628 Epoch: 4 -- Step: 6690 -- d_loss: 0.9593032598495483 -- g_loss: 1.0990207195281982 Epoch: 4 -- Step: 6700 -- d_loss: 0.9055099487304688 -- g_loss: 0.9634027481079102
Epoch: 4 -- Step: 6710 -- d_loss: 0.8647861480712891 -- g_loss: 1.2387428283691406 Epoch: 4 -- Step: 6720 -- d_loss: 1.3772281408309937 -- g_loss: 0.4274836778640747 Epoch: 4 -- Step: 6730 -- d_loss: 1.3444311618804932 -- g_loss: 0.41149652004241943 Epoch: 4 -- Step: 6740 -- d_loss: 0.8608455657958984 -- g_loss: 0.8417161703109741 Epoch: 4 -- Step: 6750 -- d_loss: 1.6831556558609009 -- g_loss: 0.28067681193351746 Epoch: 4 -- Step: 6760 -- d_loss: 1.1642847061157227 -- g_loss: 1.477705717086792 Epoch: 4 -- Step: 6770 -- d_loss: 1.6286925077438354 -- g_loss: 0.29492461681365967 Epoch: 4 -- Step: 6780 -- d_loss: 0.7269939184188843 -- g_loss: 1.3698382377624512 Epoch: 4 -- Step: 6790 -- d_loss: 0.7512883543968201 -- g_loss: 1.7134040594100952 Epoch: 4 -- Step: 6800 -- d_loss: 1.1381525993347168 -- g_loss: 0.7964102029800415
Epoch: 4 -- Step: 6810 -- d_loss: 1.5669283866882324 -- g_loss: 0.3142123520374298 Epoch: 4 -- Step: 6820 -- d_loss: 0.5648057460784912 -- g_loss: 1.6558618545532227 Epoch: 4 -- Step: 6830 -- d_loss: 0.7052483558654785 -- g_loss: 1.312842607498169 Epoch: 4 -- Step: 6840 -- d_loss: 0.515636682510376 -- g_loss: 1.4204672574996948 Epoch: 4 -- Step: 6850 -- d_loss: 0.874973714351654 -- g_loss: 1.033322811126709 Epoch: 4 -- Step: 6860 -- d_loss: 1.0840938091278076 -- g_loss: 1.1520302295684814 Epoch: 4 -- Step: 6870 -- d_loss: 1.0898313522338867 -- g_loss: 0.7989075183868408 Epoch: 4 -- Step: 6880 -- d_loss: 1.4087377786636353 -- g_loss: 0.41975775361061096 Epoch: 4 -- Step: 6890 -- d_loss: 1.288455843925476 -- g_loss: 0.516065239906311 Epoch: 4 -- Step: 6900 -- d_loss: 0.9774879813194275 -- g_loss: 0.8594238758087158
Epoch: 4 -- Step: 6910 -- d_loss: 1.1877319812774658 -- g_loss: 0.7168443202972412 Epoch: 4 -- Step: 6920 -- d_loss: 1.0646312236785889 -- g_loss: 0.986520528793335 Epoch: 4 -- Step: 6930 -- d_loss: 1.1306235790252686 -- g_loss: 0.8950362205505371 Epoch: 4 -- Step: 6940 -- d_loss: 1.0871754884719849 -- g_loss: 1.090927004814148 Epoch: 4 -- Step: 6950 -- d_loss: 1.0317471027374268 -- g_loss: 0.7152509689331055 Epoch: 4 -- Step: 6960 -- d_loss: 1.6380934715270996 -- g_loss: 0.28355735540390015 Epoch: 4 -- Step: 6970 -- d_loss: 1.1538770198822021 -- g_loss: 0.5519386529922485 Epoch: 4 -- Step: 6980 -- d_loss: 1.244994878768921 -- g_loss: 0.5136693716049194 Epoch: 4 -- Step: 6990 -- d_loss: 1.0029064416885376 -- g_loss: 0.7128620147705078 Epoch: 4 -- Step: 7000 -- d_loss: 0.33774152398109436 -- g_loss: 2.1404380798339844
Epoch: 4 -- Step: 7010 -- d_loss: 0.9426360130310059 -- g_loss: 0.7069437503814697 Epoch: 4 -- Step: 7020 -- d_loss: 1.5648975372314453 -- g_loss: 0.32003021240234375 Epoch: 4 -- Step: 7030 -- d_loss: 0.9963889718055725 -- g_loss: 0.8474361896514893 Epoch: 4 -- Step: 7040 -- d_loss: 1.155958652496338 -- g_loss: 0.683208703994751 Epoch: 4 -- Step: 7050 -- d_loss: 1.479364275932312 -- g_loss: 1.115951657295227 Epoch: 4 -- Step: 7060 -- d_loss: 0.8583966493606567 -- g_loss: 1.445767879486084 Epoch: 4 -- Step: 7070 -- d_loss: 0.8586779236793518 -- g_loss: 1.0029873847961426 Epoch: 4 -- Step: 7080 -- d_loss: 1.2019691467285156 -- g_loss: 0.489890456199646 Epoch: 4 -- Step: 7090 -- d_loss: 1.1362007856369019 -- g_loss: 0.6944493055343628 Epoch: 4 -- Step: 7100 -- d_loss: 0.7245704531669617 -- g_loss: 1.348116397857666
Epoch: 4 -- Step: 7110 -- d_loss: 1.084902048110962 -- g_loss: 0.9071055054664612 Epoch: 4 -- Step: 7120 -- d_loss: 1.09089994430542 -- g_loss: 0.9319583177566528 Epoch: 4 -- Step: 7130 -- d_loss: 1.0566997528076172 -- g_loss: 1.0286216735839844 Epoch: 4 -- Step: 7140 -- d_loss: 1.0856260061264038 -- g_loss: 1.5998518466949463 Epoch: 4 -- Step: 7150 -- d_loss: 0.9087430238723755 -- g_loss: 0.8341672420501709 Epoch: 4 -- Step: 7160 -- d_loss: 1.1874655485153198 -- g_loss: 0.5313106775283813 Epoch: 4 -- Step: 7170 -- d_loss: 1.159005880355835 -- g_loss: 0.5343914031982422 Epoch: 4 -- Step: 7180 -- d_loss: 1.0175485610961914 -- g_loss: 0.6560325622558594 Epoch: 4 -- Step: 7190 -- d_loss: 1.0323066711425781 -- g_loss: 0.6970677971839905 Epoch: 4 -- Step: 7200 -- d_loss: 1.1281816959381104 -- g_loss: 0.6001434326171875
Epoch: 4 -- Step: 7210 -- d_loss: 1.3815053701400757 -- g_loss: 1.8124009370803833 Epoch: 4 -- Step: 7220 -- d_loss: 1.0694975852966309 -- g_loss: 0.7963610887527466 Epoch: 4 -- Step: 7230 -- d_loss: 0.998275876045227 -- g_loss: 0.8084712028503418 Epoch: 4 -- Step: 7240 -- d_loss: 0.7795863151550293 -- g_loss: 1.002318024635315 Epoch: 4 -- Step: 7250 -- d_loss: 0.8579015731811523 -- g_loss: 0.8541088104248047 Epoch: 4 -- Step: 7260 -- d_loss: 0.5978658199310303 -- g_loss: 1.4510284662246704 Epoch: 4 -- Step: 7270 -- d_loss: 0.7584898471832275 -- g_loss: 1.1768805980682373 Epoch: 4 -- Step: 7280 -- d_loss: 0.7819344997406006 -- g_loss: 0.8445367217063904 Epoch: 4 -- Step: 7290 -- d_loss: 1.0007566213607788 -- g_loss: 0.7694944143295288 Epoch: 4 -- Step: 7300 -- d_loss: 1.1961441040039062 -- g_loss: 0.6076934337615967
Epoch: 4 -- Step: 7310 -- d_loss: 1.1118074655532837 -- g_loss: 0.6290270090103149 Epoch: 4 -- Step: 7320 -- d_loss: 1.4356951713562012 -- g_loss: 0.38518208265304565 Epoch: 4 -- Step: 7330 -- d_loss: 1.3885180950164795 -- g_loss: 1.0664174556732178 Epoch: 4 -- Step: 7340 -- d_loss: 1.1369709968566895 -- g_loss: 0.8908944725990295 Epoch: 4 -- Step: 7350 -- d_loss: 1.1891331672668457 -- g_loss: 0.8395596146583557 Epoch: 4 -- Step: 7360 -- d_loss: 1.1635191440582275 -- g_loss: 0.5212569832801819 Epoch: 4 -- Step: 7370 -- d_loss: 1.078273057937622 -- g_loss: 0.8006534576416016 Epoch: 4 -- Step: 7380 -- d_loss: 1.0554537773132324 -- g_loss: 0.6930011510848999 Epoch: 4 -- Step: 7390 -- d_loss: 0.9393492937088013 -- g_loss: 0.8728691935539246 Epoch: 4 -- Step: 7400 -- d_loss: 1.1780645847320557 -- g_loss: 0.6064751744270325
Epoch: 4 -- Step: 7410 -- d_loss: 0.8871992826461792 -- g_loss: 1.1573055982589722 Epoch: 4 -- Step: 7420 -- d_loss: 1.113737940788269 -- g_loss: 0.6585202217102051 Epoch: 4 -- Step: 7430 -- d_loss: 1.1036384105682373 -- g_loss: 0.6182050704956055 Epoch: 4 -- Step: 7440 -- d_loss: 1.0826059579849243 -- g_loss: 0.8236918449401855 Epoch: 4 -- Step: 7450 -- d_loss: 1.479203701019287 -- g_loss: 0.3512985408306122 Epoch: 4 -- Step: 7460 -- d_loss: 1.1770402193069458 -- g_loss: 0.5293409824371338 Epoch: 4 -- Step: 7470 -- d_loss: 0.9776275157928467 -- g_loss: 0.8218404650688171 Epoch: 4 -- Step: 7480 -- d_loss: 0.7037062644958496 -- g_loss: 1.5159318447113037 Epoch: 4 -- Step: 7490 -- d_loss: 0.9173691868782043 -- g_loss: 0.8440756797790527 Epoch: 4 -- Step: 7500 -- d_loss: 1.4653500318527222 -- g_loss: 0.4617666006088257
Epoch: 4 -- Step: 7510 -- d_loss: 1.3864275217056274 -- g_loss: 0.41479945182800293 Epoch: 4 -- Step: 7520 -- d_loss: 1.280174732208252 -- g_loss: 0.4838716685771942 Epoch: 4 -- Step: 7530 -- d_loss: 1.1998310089111328 -- g_loss: 1.1595897674560547 Epoch: 4 -- Step: 7540 -- d_loss: 0.9066548347473145 -- g_loss: 0.8710905313491821 Epoch: 4 -- Step: 7550 -- d_loss: 1.206557035446167 -- g_loss: 0.6805439591407776 Epoch: 4 -- Step: 7560 -- d_loss: 1.0765937566757202 -- g_loss: 0.7137876749038696 Epoch: 4 -- Step: 7570 -- d_loss: 1.467750906944275 -- g_loss: 1.704782247543335 Epoch: 4 -- Step: 7580 -- d_loss: 0.8609157800674438 -- g_loss: 0.893756628036499 Epoch: 4 -- Step: 7590 -- d_loss: 1.1551010608673096 -- g_loss: 0.7935304045677185 Epoch: 4 -- Step: 7600 -- d_loss: 1.4919685125350952 -- g_loss: 0.35704997181892395
Epoch: 4 -- Step: 7610 -- d_loss: 1.2562209367752075 -- g_loss: 0.5460571050643921 Epoch: 4 -- Step: 7620 -- d_loss: 1.1158180236816406 -- g_loss: 0.6173512935638428 Epoch: 4 -- Step: 7630 -- d_loss: 0.8231276869773865 -- g_loss: 1.08203125 Epoch: 4 -- Step: 7640 -- d_loss: 0.8355008959770203 -- g_loss: 0.9294366240501404 Epoch: 4 -- Step: 7650 -- d_loss: 1.2113842964172363 -- g_loss: 1.8644918203353882 Epoch: 4 -- Step: 7660 -- d_loss: 1.4791920185089111 -- g_loss: 0.3473885655403137 Epoch: 4 -- Step: 7670 -- d_loss: 1.5016947984695435 -- g_loss: 0.38949650526046753 Epoch: 4 -- Step: 7680 -- d_loss: 1.5875539779663086 -- g_loss: 0.322002649307251 Epoch: 4 -- Step: 7690 -- d_loss: 0.7893484234809875 -- g_loss: 1.468623161315918 Epoch: 4 -- Step: 7700 -- d_loss: 0.9735302329063416 -- g_loss: 1.298423171043396
Epoch: 4 -- Step: 7710 -- d_loss: 1.1938923597335815 -- g_loss: 0.9464308023452759 Epoch: 4 -- Step: 7720 -- d_loss: 1.2393274307250977 -- g_loss: 0.4908865690231323 Epoch: 4 -- Step: 7730 -- d_loss: 0.2558801770210266 -- g_loss: 3.5181570053100586 Epoch: 4 -- Step: 7740 -- d_loss: 0.5210274457931519 -- g_loss: 1.3715529441833496 Epoch: 4 -- Step: 7750 -- d_loss: 0.9750124216079712 -- g_loss: 1.0355167388916016 Epoch: 4 -- Step: 7760 -- d_loss: 1.2236582040786743 -- g_loss: 0.485474169254303 Epoch: 4 -- Step: 7770 -- d_loss: 0.7754720449447632 -- g_loss: 1.0419594049453735 Epoch: 4 -- Step: 7780 -- d_loss: 1.291416883468628 -- g_loss: 0.4504387378692627 Epoch: 4 -- Step: 7790 -- d_loss: 1.1622203588485718 -- g_loss: 0.6871954798698425 Epoch: 4 -- Step: 7800 -- d_loss: 1.2283098697662354 -- g_loss: 1.5943248271942139
Epoch: 4 -- Step: 7810 -- d_loss: 1.291853666305542 -- g_loss: 0.4499698281288147 Epoch: 4 -- Step: 7820 -- d_loss: 1.2510967254638672 -- g_loss: 0.5403247475624084 Epoch: 4 -- Step: 7830 -- d_loss: 1.4801607131958008 -- g_loss: 0.37929120659828186 Epoch: 4 -- Step: 7840 -- d_loss: 0.8077278137207031 -- g_loss: 1.271661400794983 Epoch: 4 -- Step: 7850 -- d_loss: 1.602139949798584 -- g_loss: 0.32352161407470703 Epoch: 4 -- Step: 7860 -- d_loss: 0.8283176422119141 -- g_loss: 1.2728086709976196 Epoch: 4 -- Step: 7870 -- d_loss: 1.3134785890579224 -- g_loss: 0.4529978036880493 Epoch: 4 -- Step: 7880 -- d_loss: 1.044832706451416 -- g_loss: 0.6644563674926758 Epoch: 4 -- Step: 7890 -- d_loss: 1.3162531852722168 -- g_loss: 0.47099965810775757 Epoch: 4 -- Step: 7900 -- d_loss: 1.246850609779358 -- g_loss: 0.6849435567855835
Epoch: 4 -- Step: 7910 -- d_loss: 0.6022448539733887 -- g_loss: 1.6755791902542114 Epoch: 5 -- Step: 7920 -- d_loss: 1.4099805355072021 -- g_loss: 0.5786327123641968 Epoch: 5 -- Step: 7930 -- d_loss: 0.9823029041290283 -- g_loss: 0.9400027990341187 Epoch: 5 -- Step: 7940 -- d_loss: 0.9186397790908813 -- g_loss: 0.8912849426269531 Epoch: 5 -- Step: 7950 -- d_loss: 0.9385233521461487 -- g_loss: 1.2221976518630981 Epoch: 5 -- Step: 7960 -- d_loss: 1.0490241050720215 -- g_loss: 0.7130724191665649 Epoch: 5 -- Step: 7970 -- d_loss: 1.0073671340942383 -- g_loss: 0.8322417140007019 Epoch: 5 -- Step: 7980 -- d_loss: 1.0134197473526 -- g_loss: 1.0375287532806396 Epoch: 5 -- Step: 7990 -- d_loss: 0.8033446669578552 -- g_loss: 1.3120187520980835 Epoch: 5 -- Step: 8000 -- d_loss: 1.3636492490768433 -- g_loss: 0.41580331325531006
Epoch: 5 -- Step: 8010 -- d_loss: 1.1875507831573486 -- g_loss: 0.6834365129470825 Epoch: 5 -- Step: 8020 -- d_loss: 1.0203593969345093 -- g_loss: 0.7215545773506165 Epoch: 5 -- Step: 8030 -- d_loss: 0.5897991061210632 -- g_loss: 1.2425589561462402 Epoch: 5 -- Step: 8040 -- d_loss: 0.9177379608154297 -- g_loss: 1.278610110282898 Epoch: 5 -- Step: 8050 -- d_loss: 1.5457810163497925 -- g_loss: 0.9775320291519165 Epoch: 5 -- Step: 8060 -- d_loss: 1.0055932998657227 -- g_loss: 1.1182734966278076 Epoch: 5 -- Step: 8070 -- d_loss: 0.8627122640609741 -- g_loss: 0.9629484415054321 Epoch: 5 -- Step: 8080 -- d_loss: 0.9727851152420044 -- g_loss: 1.1777740716934204 Epoch: 5 -- Step: 8090 -- d_loss: 0.9298640489578247 -- g_loss: 0.7770636081695557 Epoch: 5 -- Step: 8100 -- d_loss: 0.8106931447982788 -- g_loss: 0.9051065444946289
Epoch: 5 -- Step: 8110 -- d_loss: 1.1521517038345337 -- g_loss: 0.5998618602752686 Epoch: 5 -- Step: 8120 -- d_loss: 1.1865894794464111 -- g_loss: 1.3572262525558472 Epoch: 5 -- Step: 8130 -- d_loss: 0.9685280323028564 -- g_loss: 0.8315102458000183 Epoch: 5 -- Step: 8140 -- d_loss: 1.381431221961975 -- g_loss: 0.4250659942626953 Epoch: 5 -- Step: 8150 -- d_loss: 1.1555798053741455 -- g_loss: 0.6401737928390503 Epoch: 5 -- Step: 8160 -- d_loss: 1.6263253688812256 -- g_loss: 0.3288499116897583 Epoch: 5 -- Step: 8170 -- d_loss: 1.421604871749878 -- g_loss: 0.4007619023323059 Epoch: 5 -- Step: 8180 -- d_loss: 1.1562546491622925 -- g_loss: 0.5597179532051086 Epoch: 5 -- Step: 8190 -- d_loss: 0.9638112187385559 -- g_loss: 1.4794727563858032 Epoch: 5 -- Step: 8200 -- d_loss: 1.7418925762176514 -- g_loss: 0.279845654964447
Epoch: 5 -- Step: 8210 -- d_loss: 1.1942877769470215 -- g_loss: 0.6925122737884521 Epoch: 5 -- Step: 8220 -- d_loss: 1.177901029586792 -- g_loss: 0.6937172412872314 Epoch: 5 -- Step: 8230 -- d_loss: 1.0672053098678589 -- g_loss: 0.8280134797096252 Epoch: 5 -- Step: 8240 -- d_loss: 1.267927885055542 -- g_loss: 0.547391414642334 Epoch: 5 -- Step: 8250 -- d_loss: 1.0659193992614746 -- g_loss: 0.6845631003379822 Epoch: 5 -- Step: 8260 -- d_loss: 2.787950038909912 -- g_loss: 0.09443259239196777 Epoch: 5 -- Step: 8270 -- d_loss: 1.3621983528137207 -- g_loss: 0.45838385820388794 Epoch: 5 -- Step: 8280 -- d_loss: 1.209120273590088 -- g_loss: 0.7387605309486389 Epoch: 5 -- Step: 8290 -- d_loss: 0.7626805305480957 -- g_loss: 1.1308631896972656 Epoch: 5 -- Step: 8300 -- d_loss: 1.0669264793395996 -- g_loss: 0.7781090140342712
Epoch: 5 -- Step: 8310 -- d_loss: 1.3435921669006348 -- g_loss: 0.44047263264656067 Epoch: 5 -- Step: 8320 -- d_loss: 1.2590724229812622 -- g_loss: 0.48454996943473816 Epoch: 5 -- Step: 8330 -- d_loss: 1.8264691829681396 -- g_loss: 0.25679972767829895 Epoch: 5 -- Step: 8340 -- d_loss: 1.0005537271499634 -- g_loss: 0.9298606514930725 Epoch: 5 -- Step: 8350 -- d_loss: 1.1849334239959717 -- g_loss: 0.5378950834274292 Epoch: 5 -- Step: 8360 -- d_loss: 0.47396793961524963 -- g_loss: 1.486276626586914 Epoch: 5 -- Step: 8370 -- d_loss: 1.0077818632125854 -- g_loss: 0.6417669653892517 Epoch: 5 -- Step: 8380 -- d_loss: 0.9277564287185669 -- g_loss: 0.7935541868209839 Epoch: 5 -- Step: 8390 -- d_loss: 1.1480708122253418 -- g_loss: 0.659305214881897 Epoch: 5 -- Step: 8400 -- d_loss: 1.0601954460144043 -- g_loss: 1.8524664640426636
Epoch: 5 -- Step: 8410 -- d_loss: 1.3001000881195068 -- g_loss: 1.291379451751709 Epoch: 5 -- Step: 8420 -- d_loss: 1.0122309923171997 -- g_loss: 1.0050599575042725 Epoch: 5 -- Step: 8430 -- d_loss: 1.182517409324646 -- g_loss: 2.3005785942077637 Epoch: 5 -- Step: 8440 -- d_loss: 0.8174502849578857 -- g_loss: 0.9572389125823975 Epoch: 5 -- Step: 8450 -- d_loss: 0.4563172459602356 -- g_loss: 1.650376558303833 Epoch: 5 -- Step: 8460 -- d_loss: 1.0123376846313477 -- g_loss: 0.659896731376648 Epoch: 5 -- Step: 8470 -- d_loss: 1.0995876789093018 -- g_loss: 0.7274157404899597 Epoch: 5 -- Step: 8480 -- d_loss: 0.8986325263977051 -- g_loss: 0.8516784310340881 Epoch: 5 -- Step: 8490 -- d_loss: 1.194273829460144 -- g_loss: 0.9272841215133667 Epoch: 5 -- Step: 8500 -- d_loss: 1.4141075611114502 -- g_loss: 0.41076821088790894
Epoch: 5 -- Step: 8510 -- d_loss: 1.5997673273086548 -- g_loss: 1.504267930984497 Epoch: 5 -- Step: 8520 -- d_loss: 1.1631826162338257 -- g_loss: 0.6903986930847168 Epoch: 5 -- Step: 8530 -- d_loss: 1.0835607051849365 -- g_loss: 0.9786221385002136 Epoch: 5 -- Step: 8540 -- d_loss: 1.2295992374420166 -- g_loss: 0.5756673812866211 Epoch: 5 -- Step: 8550 -- d_loss: 1.3444658517837524 -- g_loss: 0.4589982330799103 Epoch: 5 -- Step: 8560 -- d_loss: 1.0096505880355835 -- g_loss: 0.8496685028076172 Epoch: 5 -- Step: 8570 -- d_loss: 1.16233229637146 -- g_loss: 1.2056143283843994 Epoch: 5 -- Step: 8580 -- d_loss: 1.066266655921936 -- g_loss: 0.8909822702407837 Epoch: 5 -- Step: 8590 -- d_loss: 1.5951781272888184 -- g_loss: 0.3501206636428833 Epoch: 5 -- Step: 8600 -- d_loss: 1.1848626136779785 -- g_loss: 0.5771867036819458
Epoch: 5 -- Step: 8610 -- d_loss: 1.8121106624603271 -- g_loss: 0.2365587055683136 Epoch: 5 -- Step: 8620 -- d_loss: 1.200430154800415 -- g_loss: 0.5846844911575317 Epoch: 5 -- Step: 8630 -- d_loss: 0.9621585011482239 -- g_loss: 1.3000816106796265 Epoch: 5 -- Step: 8640 -- d_loss: 1.2489125728607178 -- g_loss: 0.49990683794021606 Epoch: 5 -- Step: 8650 -- d_loss: 0.8849258422851562 -- g_loss: 1.2177985906600952 Epoch: 5 -- Step: 8660 -- d_loss: 1.0931380987167358 -- g_loss: 0.7846114039421082 Epoch: 5 -- Step: 8670 -- d_loss: 1.1872591972351074 -- g_loss: 0.5182570815086365 Epoch: 5 -- Step: 8680 -- d_loss: 1.2197744846343994 -- g_loss: 1.672605276107788 Epoch: 5 -- Step: 8690 -- d_loss: 1.218421220779419 -- g_loss: 0.46941715478897095 Epoch: 5 -- Step: 8700 -- d_loss: 1.3678867816925049 -- g_loss: 0.40649181604385376
Epoch: 5 -- Step: 8710 -- d_loss: 1.0379656553268433 -- g_loss: 0.9845525622367859 Epoch: 5 -- Step: 8720 -- d_loss: 1.1087952852249146 -- g_loss: 0.5960259437561035 Epoch: 5 -- Step: 8730 -- d_loss: 1.2094706296920776 -- g_loss: 0.532056450843811 Epoch: 5 -- Step: 8740 -- d_loss: 0.8786411285400391 -- g_loss: 2.0172109603881836 Epoch: 5 -- Step: 8750 -- d_loss: 1.1496058702468872 -- g_loss: 1.0769164562225342 Epoch: 5 -- Step: 8760 -- d_loss: 1.016504168510437 -- g_loss: 0.9600406885147095 Epoch: 5 -- Step: 8770 -- d_loss: 1.064955711364746 -- g_loss: 0.9430133104324341 Epoch: 5 -- Step: 8780 -- d_loss: 1.1089191436767578 -- g_loss: 0.8956736326217651 Epoch: 5 -- Step: 8790 -- d_loss: 1.0600939989089966 -- g_loss: 0.6706002950668335 Epoch: 5 -- Step: 8800 -- d_loss: 0.9383327960968018 -- g_loss: 0.7883384227752686
Epoch: 5 -- Step: 8810 -- d_loss: 0.7397220134735107 -- g_loss: 0.926487922668457 Epoch: 5 -- Step: 8820 -- d_loss: 1.1610782146453857 -- g_loss: 0.9850960969924927 Epoch: 5 -- Step: 8830 -- d_loss: 1.403876543045044 -- g_loss: 0.4085614085197449 Epoch: 5 -- Step: 8840 -- d_loss: 1.8471596240997314 -- g_loss: 1.982452154159546 Epoch: 5 -- Step: 8850 -- d_loss: 1.0073238611221313 -- g_loss: 0.9716662168502808 Epoch: 5 -- Step: 8860 -- d_loss: 1.1153233051300049 -- g_loss: 0.7344145774841309 Epoch: 5 -- Step: 8870 -- d_loss: 1.2272319793701172 -- g_loss: 0.7919542789459229 Epoch: 5 -- Step: 8880 -- d_loss: 1.124225378036499 -- g_loss: 0.583987832069397 Epoch: 5 -- Step: 8890 -- d_loss: 1.1387451887130737 -- g_loss: 0.6776858568191528 Epoch: 5 -- Step: 8900 -- d_loss: 0.9293128252029419 -- g_loss: 0.8722370266914368
Epoch: 5 -- Step: 8910 -- d_loss: 0.9306785464286804 -- g_loss: 1.014467477798462 Epoch: 5 -- Step: 8920 -- d_loss: 1.0661499500274658 -- g_loss: 0.9287909269332886 Epoch: 5 -- Step: 8930 -- d_loss: 1.3573298454284668 -- g_loss: 0.47157007455825806 Epoch: 5 -- Step: 8940 -- d_loss: 0.6812368035316467 -- g_loss: 1.1292593479156494 Epoch: 5 -- Step: 8950 -- d_loss: 0.9877896904945374 -- g_loss: 0.88847815990448 Epoch: 5 -- Step: 8960 -- d_loss: 1.4456658363342285 -- g_loss: 0.4352240264415741 Epoch: 5 -- Step: 8970 -- d_loss: 0.9662722945213318 -- g_loss: 0.9268842339515686 Epoch: 5 -- Step: 8980 -- d_loss: 0.9806637763977051 -- g_loss: 1.468684196472168 Epoch: 5 -- Step: 8990 -- d_loss: 0.790969967842102 -- g_loss: 1.0401391983032227 Epoch: 5 -- Step: 9000 -- d_loss: 1.202648401260376 -- g_loss: 1.286025047302246
Epoch: 5 -- Step: 9010 -- d_loss: 1.4856282472610474 -- g_loss: 0.3717537522315979 Epoch: 5 -- Step: 9020 -- d_loss: 1.0834283828735352 -- g_loss: 1.4829195737838745 Epoch: 5 -- Step: 9030 -- d_loss: 1.22648286819458 -- g_loss: 0.5674535036087036 Epoch: 5 -- Step: 9040 -- d_loss: 1.458998441696167 -- g_loss: 0.47273021936416626 Epoch: 5 -- Step: 9050 -- d_loss: 1.0880074501037598 -- g_loss: 0.7362834811210632 Epoch: 5 -- Step: 9060 -- d_loss: 1.0719456672668457 -- g_loss: 0.6881409287452698 Epoch: 5 -- Step: 9070 -- d_loss: 1.1359808444976807 -- g_loss: 0.7056482434272766 Epoch: 5 -- Step: 9080 -- d_loss: 0.9792712926864624 -- g_loss: 1.1108964681625366 Epoch: 5 -- Step: 9090 -- d_loss: 0.9004079103469849 -- g_loss: 0.808344304561615 Epoch: 5 -- Step: 9100 -- d_loss: 1.1445621252059937 -- g_loss: 0.5696436166763306
Epoch: 5 -- Step: 9110 -- d_loss: 0.9021444320678711 -- g_loss: 0.8364893794059753 Epoch: 5 -- Step: 9120 -- d_loss: 1.036270022392273 -- g_loss: 0.624390184879303 Epoch: 5 -- Step: 9130 -- d_loss: 0.9265369176864624 -- g_loss: 0.7484949827194214 Epoch: 5 -- Step: 9140 -- d_loss: 0.7802270650863647 -- g_loss: 0.9961349368095398 Epoch: 5 -- Step: 9150 -- d_loss: 1.1673202514648438 -- g_loss: 1.5262203216552734 Epoch: 5 -- Step: 9160 -- d_loss: 1.0193992853164673 -- g_loss: 0.8815162777900696 Epoch: 5 -- Step: 9170 -- d_loss: 0.832447350025177 -- g_loss: 1.2469395399093628 Epoch: 5 -- Step: 9180 -- d_loss: 1.5683801174163818 -- g_loss: 0.2976978123188019 Epoch: 5 -- Step: 9190 -- d_loss: 1.2242376804351807 -- g_loss: 0.5666037797927856 Epoch: 5 -- Step: 9200 -- d_loss: 1.243249773979187 -- g_loss: 0.4880404770374298
Epoch: 5 -- Step: 9210 -- d_loss: 1.1944035291671753 -- g_loss: 0.6820403337478638 Epoch: 5 -- Step: 9220 -- d_loss: 0.8439964056015015 -- g_loss: 0.8305495977401733 Epoch: 5 -- Step: 9230 -- d_loss: 1.083107829093933 -- g_loss: 1.1314833164215088 Epoch: 5 -- Step: 9240 -- d_loss: 1.090395450592041 -- g_loss: 0.6673893928527832 Epoch: 5 -- Step: 9250 -- d_loss: 0.8517144322395325 -- g_loss: 0.8414717316627502 Epoch: 5 -- Step: 9260 -- d_loss: 1.0118809938430786 -- g_loss: 1.349705457687378 Epoch: 5 -- Step: 9270 -- d_loss: 1.1366101503372192 -- g_loss: 0.8484644889831543 Epoch: 5 -- Step: 9280 -- d_loss: 1.8865562677383423 -- g_loss: 0.21492211520671844 Epoch: 5 -- Step: 9290 -- d_loss: 1.0312522649765015 -- g_loss: 0.8093420267105103 Epoch: 5 -- Step: 9300 -- d_loss: 1.189107894897461 -- g_loss: 1.4584006071090698
Epoch: 5 -- Step: 9310 -- d_loss: 1.121558666229248 -- g_loss: 0.6730690002441406 Epoch: 5 -- Step: 9320 -- d_loss: 0.988076388835907 -- g_loss: 1.0188202857971191 Epoch: 5 -- Step: 9330 -- d_loss: 1.0210597515106201 -- g_loss: 1.0356028079986572 Epoch: 5 -- Step: 9340 -- d_loss: 1.1374636888504028 -- g_loss: 0.6347975134849548 Epoch: 5 -- Step: 9350 -- d_loss: 1.1839345693588257 -- g_loss: 1.3035476207733154 Epoch: 5 -- Step: 9360 -- d_loss: 2.2113595008850098 -- g_loss: 0.15978537499904633 Epoch: 5 -- Step: 9370 -- d_loss: 1.155302882194519 -- g_loss: 0.6544202566146851 Epoch: 5 -- Step: 9380 -- d_loss: 1.186290979385376 -- g_loss: 0.5037459135055542 Epoch: 5 -- Step: 9390 -- d_loss: 1.8479385375976562 -- g_loss: 0.23827886581420898 Epoch: 5 -- Step: 9400 -- d_loss: 1.146174669265747 -- g_loss: 1.157567024230957
Epoch: 5 -- Step: 9410 -- d_loss: 1.420480489730835 -- g_loss: 0.7198437452316284 Epoch: 5 -- Step: 9420 -- d_loss: 1.231819987297058 -- g_loss: 0.5987565517425537 Epoch: 5 -- Step: 9430 -- d_loss: 1.0104529857635498 -- g_loss: 0.6108200550079346 Epoch: 5 -- Step: 9440 -- d_loss: 0.9918586015701294 -- g_loss: 0.8707661628723145 Epoch: 5 -- Step: 9450 -- d_loss: 0.9536339044570923 -- g_loss: 0.706335186958313 Epoch: 5 -- Step: 9460 -- d_loss: 1.039074182510376 -- g_loss: 0.6976483464241028 Epoch: 5 -- Step: 9470 -- d_loss: 1.0790950059890747 -- g_loss: 0.6608728170394897 Epoch: 5 -- Step: 9480 -- d_loss: 1.6557199954986572 -- g_loss: 0.2963612675666809 Epoch: 5 -- Step: 9490 -- d_loss: 1.310699224472046 -- g_loss: 0.5132936239242554 Epoch: 6 -- Step: 9500 -- d_loss: 1.0367987155914307 -- g_loss: 1.4001810550689697
Epoch: 6 -- Step: 9510 -- d_loss: 0.8271576166152954 -- g_loss: 1.0781211853027344 Epoch: 6 -- Step: 9520 -- d_loss: 0.6462904214859009 -- g_loss: 1.177174687385559 Epoch: 6 -- Step: 9530 -- d_loss: 1.004761815071106 -- g_loss: 0.8765353560447693 Epoch: 6 -- Step: 9540 -- d_loss: 1.109116792678833 -- g_loss: 0.9133995771408081 Epoch: 6 -- Step: 9550 -- d_loss: 0.45571351051330566 -- g_loss: 1.881594181060791 Epoch: 6 -- Step: 9560 -- d_loss: 1.5520708560943604 -- g_loss: 0.32838231325149536 Epoch: 6 -- Step: 9570 -- d_loss: 0.932131826877594 -- g_loss: 0.9153972864151001 Epoch: 6 -- Step: 9580 -- d_loss: 1.135466456413269 -- g_loss: 0.675335168838501 Epoch: 6 -- Step: 9590 -- d_loss: 1.0451213121414185 -- g_loss: 0.7735823392868042 Epoch: 6 -- Step: 9600 -- d_loss: 1.105011224746704 -- g_loss: 0.6811210513114929
Epoch: 6 -- Step: 9610 -- d_loss: 1.1550053358078003 -- g_loss: 1.4921457767486572 Epoch: 6 -- Step: 9620 -- d_loss: 0.9321411848068237 -- g_loss: 0.8701598048210144 Epoch: 6 -- Step: 9630 -- d_loss: 1.0115681886672974 -- g_loss: 0.83366858959198 Epoch: 6 -- Step: 9640 -- d_loss: 0.858536422252655 -- g_loss: 0.993654727935791 Epoch: 6 -- Step: 9650 -- d_loss: 1.1882851123809814 -- g_loss: 0.9968938231468201 Epoch: 6 -- Step: 9660 -- d_loss: 1.241889476776123 -- g_loss: 0.5621843338012695 Epoch: 6 -- Step: 9670 -- d_loss: 1.3237578868865967 -- g_loss: 0.4887056350708008 Epoch: 6 -- Step: 9680 -- d_loss: 0.9953431487083435 -- g_loss: 0.7870841026306152 Epoch: 6 -- Step: 9690 -- d_loss: 0.9809389114379883 -- g_loss: 1.812981367111206 Epoch: 6 -- Step: 9700 -- d_loss: 1.0192091464996338 -- g_loss: 1.07570219039917
Epoch: 6 -- Step: 9710 -- d_loss: 1.2674908638000488 -- g_loss: 0.49262547492980957 Epoch: 6 -- Step: 9720 -- d_loss: 1.2829980850219727 -- g_loss: 0.4647483229637146 Epoch: 6 -- Step: 9730 -- d_loss: 1.036360740661621 -- g_loss: 0.7117831707000732 Epoch: 6 -- Step: 9740 -- d_loss: 1.3109958171844482 -- g_loss: 0.4981688857078552 Epoch: 6 -- Step: 9750 -- d_loss: 1.411397099494934 -- g_loss: 0.38158124685287476 Epoch: 6 -- Step: 9760 -- d_loss: 0.8315688967704773 -- g_loss: 1.0435538291931152 Epoch: 6 -- Step: 9770 -- d_loss: 1.6031328439712524 -- g_loss: 0.3264370858669281 Epoch: 6 -- Step: 9780 -- d_loss: 1.160602331161499 -- g_loss: 0.7349621057510376 Epoch: 6 -- Step: 9790 -- d_loss: 1.2265050411224365 -- g_loss: 0.6642871499061584 Epoch: 6 -- Step: 9800 -- d_loss: 1.193233609199524 -- g_loss: 0.5783209204673767
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Epoch: 6 -- Step: 10010 -- d_loss: 1.6675777435302734 -- g_loss: 0.2979089617729187 Epoch: 6 -- Step: 10020 -- d_loss: 0.741628885269165 -- g_loss: 1.0806891918182373 Epoch: 6 -- Step: 10030 -- d_loss: 0.9018085598945618 -- g_loss: 0.7727941870689392 Epoch: 6 -- Step: 10040 -- d_loss: 0.6137226819992065 -- g_loss: 1.58476984500885 Epoch: 6 -- Step: 10050 -- d_loss: 1.025289535522461 -- g_loss: 0.8959510326385498 Epoch: 6 -- Step: 10060 -- d_loss: 1.3180530071258545 -- g_loss: 0.5580102801322937 Epoch: 6 -- Step: 10070 -- d_loss: 0.703572154045105 -- g_loss: 1.4798001050949097 Epoch: 6 -- Step: 10080 -- d_loss: 0.8967299461364746 -- g_loss: 1.247682809829712 Epoch: 6 -- Step: 10090 -- d_loss: 1.504756212234497 -- g_loss: 0.39327478408813477 Epoch: 6 -- Step: 10100 -- d_loss: 0.9076992273330688 -- g_loss: 1.1102246046066284
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Epoch: 6 -- Step: 10310 -- d_loss: 1.1798748970031738 -- g_loss: 0.9213124513626099 Epoch: 6 -- Step: 10320 -- d_loss: 0.9115083813667297 -- g_loss: 0.8056082725524902 Epoch: 6 -- Step: 10330 -- d_loss: 1.4427486658096313 -- g_loss: 0.4152190685272217 Epoch: 6 -- Step: 10340 -- d_loss: 0.916265606880188 -- g_loss: 0.9466313719749451 Epoch: 6 -- Step: 10350 -- d_loss: 0.8897536993026733 -- g_loss: 1.139754056930542 Epoch: 6 -- Step: 10360 -- d_loss: 1.2091238498687744 -- g_loss: 0.5461490750312805 Epoch: 6 -- Step: 10370 -- d_loss: 0.9638240337371826 -- g_loss: 1.1055642366409302 Epoch: 6 -- Step: 10380 -- d_loss: 1.2884776592254639 -- g_loss: 0.4450017809867859 Epoch: 6 -- Step: 10390 -- d_loss: 0.7364583611488342 -- g_loss: 1.0651804208755493 Epoch: 6 -- Step: 10400 -- d_loss: 1.1825419664382935 -- g_loss: 1.3189576864242554
Epoch: 6 -- Step: 10410 -- d_loss: 1.388170599937439 -- g_loss: 0.3794902265071869 Epoch: 6 -- Step: 10420 -- d_loss: 1.2028645277023315 -- g_loss: 0.7830238938331604 Epoch: 6 -- Step: 10430 -- d_loss: 1.0395467281341553 -- g_loss: 0.7430664300918579 Epoch: 6 -- Step: 10440 -- d_loss: 0.9458557963371277 -- g_loss: 0.7709637880325317 Epoch: 6 -- Step: 10450 -- d_loss: 0.9551182985305786 -- g_loss: 0.765087366104126 Epoch: 6 -- Step: 10460 -- d_loss: 1.3228728771209717 -- g_loss: 0.42839980125427246 Epoch: 6 -- Step: 10470 -- d_loss: 1.0512659549713135 -- g_loss: 0.6661204695701599 Epoch: 6 -- Step: 10480 -- d_loss: 1.1560959815979004 -- g_loss: 0.6176644563674927 Epoch: 6 -- Step: 10490 -- d_loss: 1.4932925701141357 -- g_loss: 0.40621042251586914 Epoch: 6 -- Step: 10500 -- d_loss: 1.7769616842269897 -- g_loss: 0.2531895339488983
Epoch: 6 -- Step: 10510 -- d_loss: 1.0290095806121826 -- g_loss: 0.8845224380493164 Epoch: 6 -- Step: 10520 -- d_loss: 0.9932047128677368 -- g_loss: 1.2572216987609863 Epoch: 6 -- Step: 10530 -- d_loss: 1.8850035667419434 -- g_loss: 0.23026296496391296 Epoch: 6 -- Step: 10540 -- d_loss: 0.9046441912651062 -- g_loss: 0.9875686168670654 Epoch: 6 -- Step: 10550 -- d_loss: 0.9147394895553589 -- g_loss: 0.959117591381073 Epoch: 6 -- Step: 10560 -- d_loss: 1.1592388153076172 -- g_loss: 0.7775955200195312 Epoch: 6 -- Step: 10570 -- d_loss: 1.0930805206298828 -- g_loss: 0.6278856992721558 Epoch: 6 -- Step: 10580 -- d_loss: 1.1943483352661133 -- g_loss: 0.9707492589950562 Epoch: 6 -- Step: 10590 -- d_loss: 1.2506155967712402 -- g_loss: 0.5174360275268555 Epoch: 6 -- Step: 10600 -- d_loss: 1.2157039642333984 -- g_loss: 0.6615425944328308
Epoch: 6 -- Step: 10610 -- d_loss: 0.765215277671814 -- g_loss: 1.1952673196792603 Epoch: 6 -- Step: 10620 -- d_loss: 1.300635814666748 -- g_loss: 0.4509645402431488 Epoch: 6 -- Step: 10630 -- d_loss: 1.0111393928527832 -- g_loss: 0.851613461971283 Epoch: 6 -- Step: 10640 -- d_loss: 1.0159797668457031 -- g_loss: 1.1081061363220215 Epoch: 6 -- Step: 10650 -- d_loss: 0.8861043453216553 -- g_loss: 0.949914276599884 Epoch: 6 -- Step: 10660 -- d_loss: 1.3558564186096191 -- g_loss: 0.42587393522262573 Epoch: 6 -- Step: 10670 -- d_loss: 1.0475481748580933 -- g_loss: 0.8243423700332642 Epoch: 6 -- Step: 10680 -- d_loss: 1.3297338485717773 -- g_loss: 1.5384979248046875 Epoch: 6 -- Step: 10690 -- d_loss: 1.206282138824463 -- g_loss: 0.5942723751068115 Epoch: 6 -- Step: 10700 -- d_loss: 1.1376895904541016 -- g_loss: 0.6317090392112732
Epoch: 6 -- Step: 10710 -- d_loss: 1.0941863059997559 -- g_loss: 0.8472905158996582 Epoch: 6 -- Step: 10720 -- d_loss: 1.0478382110595703 -- g_loss: 0.7249155044555664 Epoch: 6 -- Step: 10730 -- d_loss: 1.2206954956054688 -- g_loss: 0.4521576762199402 Epoch: 6 -- Step: 10740 -- d_loss: 1.4980592727661133 -- g_loss: 1.7154796123504639 Epoch: 6 -- Step: 10750 -- d_loss: 1.3226981163024902 -- g_loss: 0.4733259081840515 Epoch: 6 -- Step: 10760 -- d_loss: 1.0330541133880615 -- g_loss: 1.063737392425537 Epoch: 6 -- Step: 10770 -- d_loss: 1.2506413459777832 -- g_loss: 0.7143725752830505 Epoch: 6 -- Step: 10780 -- d_loss: 1.1212958097457886 -- g_loss: 0.5727735161781311 Epoch: 6 -- Step: 10790 -- d_loss: 0.8428019881248474 -- g_loss: 1.2967950105667114 Epoch: 6 -- Step: 10800 -- d_loss: 1.1640892028808594 -- g_loss: 0.716937243938446
Epoch: 6 -- Step: 10810 -- d_loss: 0.9354768991470337 -- g_loss: 0.7956169843673706 Epoch: 6 -- Step: 10820 -- d_loss: 0.8782028555870056 -- g_loss: 1.0347659587860107 Epoch: 6 -- Step: 10830 -- d_loss: 1.1518611907958984 -- g_loss: 0.6169061660766602 Epoch: 6 -- Step: 10840 -- d_loss: 1.1333587169647217 -- g_loss: 0.6873321533203125 Epoch: 6 -- Step: 10850 -- d_loss: 0.8896603584289551 -- g_loss: 1.5116759538650513 Epoch: 6 -- Step: 10860 -- d_loss: 1.4890387058258057 -- g_loss: 0.3488501310348511 Epoch: 6 -- Step: 10870 -- d_loss: 1.4845271110534668 -- g_loss: 0.3647153675556183 Epoch: 6 -- Step: 10880 -- d_loss: 1.066452145576477 -- g_loss: 0.8096418380737305 Epoch: 6 -- Step: 10890 -- d_loss: 0.6955103874206543 -- g_loss: 1.3856829404830933 Epoch: 6 -- Step: 10900 -- d_loss: 1.1003448963165283 -- g_loss: 0.5715078115463257
Epoch: 6 -- Step: 10910 -- d_loss: 0.99664306640625 -- g_loss: 0.887243390083313 Epoch: 6 -- Step: 10920 -- d_loss: 0.8523561358451843 -- g_loss: 1.1916513442993164 Epoch: 6 -- Step: 10930 -- d_loss: 1.2380121946334839 -- g_loss: 0.669628381729126 Epoch: 6 -- Step: 10940 -- d_loss: 1.129908800125122 -- g_loss: 0.6960366368293762 Epoch: 6 -- Step: 10950 -- d_loss: 1.2721143960952759 -- g_loss: 0.4856571555137634 Epoch: 6 -- Step: 10960 -- d_loss: 1.018924355506897 -- g_loss: 0.7289411425590515 Epoch: 6 -- Step: 10970 -- d_loss: 1.0967581272125244 -- g_loss: 0.7390775680541992 Epoch: 6 -- Step: 10980 -- d_loss: 1.1378180980682373 -- g_loss: 0.5885270833969116 Epoch: 6 -- Step: 10990 -- d_loss: 1.44753098487854 -- g_loss: 0.3896762430667877 Epoch: 6 -- Step: 11000 -- d_loss: 1.0031251907348633 -- g_loss: 1.4649103879928589
Epoch: 6 -- Step: 11010 -- d_loss: 1.250216007232666 -- g_loss: 0.6818251013755798 Epoch: 6 -- Step: 11020 -- d_loss: 1.1609766483306885 -- g_loss: 0.8650136590003967 Epoch: 6 -- Step: 11030 -- d_loss: 0.8658730983734131 -- g_loss: 0.9296154975891113 Epoch: 6 -- Step: 11040 -- d_loss: 1.236820936203003 -- g_loss: 0.79111647605896 Epoch: 6 -- Step: 11050 -- d_loss: 1.3295186758041382 -- g_loss: 0.4402153491973877 Epoch: 6 -- Step: 11060 -- d_loss: 1.1830716133117676 -- g_loss: 1.3303173780441284 Epoch: 6 -- Step: 11070 -- d_loss: 1.2542099952697754 -- g_loss: 0.544526994228363 Epoch: 7 -- Step: 11080 -- d_loss: 0.9744337797164917 -- g_loss: 0.9232884049415588 Epoch: 7 -- Step: 11090 -- d_loss: 0.8487421274185181 -- g_loss: 1.3870162963867188 Epoch: 7 -- Step: 11100 -- d_loss: 0.808927595615387 -- g_loss: 0.8372699022293091
Epoch: 7 -- Step: 11110 -- d_loss: 1.0321251153945923 -- g_loss: 0.7046724557876587 Epoch: 7 -- Step: 11120 -- d_loss: 0.9955115914344788 -- g_loss: 1.246889591217041 Epoch: 7 -- Step: 11130 -- d_loss: 1.4318994283676147 -- g_loss: 0.4650779068470001 Epoch: 7 -- Step: 11140 -- d_loss: 1.0344164371490479 -- g_loss: 0.7263221740722656 Epoch: 7 -- Step: 11150 -- d_loss: 1.5657597780227661 -- g_loss: 0.46854087710380554 Epoch: 7 -- Step: 11160 -- d_loss: 1.018568515777588 -- g_loss: 1.046837329864502 Epoch: 7 -- Step: 11170 -- d_loss: 1.159820556640625 -- g_loss: 0.982932448387146 Epoch: 7 -- Step: 11180 -- d_loss: 1.2624914646148682 -- g_loss: 0.9252070784568787 Epoch: 7 -- Step: 11190 -- d_loss: 0.7811667919158936 -- g_loss: 1.3944872617721558 Epoch: 7 -- Step: 11200 -- d_loss: 1.7593756914138794 -- g_loss: 0.26786959171295166
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Epoch: 7 -- Step: 12210 -- d_loss: 1.331820011138916 -- g_loss: 2.1809120178222656 Epoch: 7 -- Step: 12220 -- d_loss: 1.0093536376953125 -- g_loss: 0.9519433975219727 Epoch: 7 -- Step: 12230 -- d_loss: 1.2996025085449219 -- g_loss: 0.42632097005844116 Epoch: 7 -- Step: 12240 -- d_loss: 1.1837323904037476 -- g_loss: 0.623096764087677 Epoch: 7 -- Step: 12250 -- d_loss: 1.2569106817245483 -- g_loss: 0.46964526176452637 Epoch: 7 -- Step: 12260 -- d_loss: 1.0598907470703125 -- g_loss: 1.0876237154006958 Epoch: 7 -- Step: 12270 -- d_loss: 1.070013403892517 -- g_loss: 0.6512229442596436 Epoch: 7 -- Step: 12280 -- d_loss: 0.8819301724433899 -- g_loss: 0.9638974666595459 Epoch: 7 -- Step: 12290 -- d_loss: 1.1348261833190918 -- g_loss: 0.6368581652641296 Epoch: 7 -- Step: 12300 -- d_loss: 1.1403800249099731 -- g_loss: 0.5460889339447021
Epoch: 7 -- Step: 12310 -- d_loss: 1.2295979261398315 -- g_loss: 0.6874212026596069 Epoch: 7 -- Step: 12320 -- d_loss: 0.9694486260414124 -- g_loss: 0.9141362309455872 Epoch: 7 -- Step: 12330 -- d_loss: 0.8573565483093262 -- g_loss: 0.9613305926322937 Epoch: 7 -- Step: 12340 -- d_loss: 0.8229280114173889 -- g_loss: 1.1141512393951416 Epoch: 7 -- Step: 12350 -- d_loss: 1.072229266166687 -- g_loss: 1.210893154144287 Epoch: 7 -- Step: 12360 -- d_loss: 0.8411649465560913 -- g_loss: 0.8855013847351074 Epoch: 7 -- Step: 12370 -- d_loss: 0.9276137948036194 -- g_loss: 0.7464712858200073 Epoch: 7 -- Step: 12380 -- d_loss: 1.1134576797485352 -- g_loss: 0.6231322288513184 Epoch: 7 -- Step: 12390 -- d_loss: 1.0208903551101685 -- g_loss: 0.6353693008422852 Epoch: 7 -- Step: 12400 -- d_loss: 1.0962965488433838 -- g_loss: 0.6912500858306885
Epoch: 7 -- Step: 12410 -- d_loss: 1.143533706665039 -- g_loss: 0.5643879175186157 Epoch: 7 -- Step: 12420 -- d_loss: 1.126146674156189 -- g_loss: 0.6254990696907043 Epoch: 7 -- Step: 12430 -- d_loss: 1.0776898860931396 -- g_loss: 0.8272290825843811 Epoch: 7 -- Step: 12440 -- d_loss: 1.136488914489746 -- g_loss: 0.9487131834030151 Epoch: 7 -- Step: 12450 -- d_loss: 0.994693398475647 -- g_loss: 0.7710354924201965 Epoch: 7 -- Step: 12460 -- d_loss: 1.343679666519165 -- g_loss: 1.4697628021240234 Epoch: 7 -- Step: 12470 -- d_loss: 1.376383662223816 -- g_loss: 0.40262675285339355 Epoch: 7 -- Step: 12480 -- d_loss: 1.1270413398742676 -- g_loss: 0.9733539819717407 Epoch: 7 -- Step: 12490 -- d_loss: 1.0395601987838745 -- g_loss: 0.9820035696029663 Epoch: 7 -- Step: 12500 -- d_loss: 1.1242587566375732 -- g_loss: 1.0389885902404785
Epoch: 7 -- Step: 12510 -- d_loss: 1.4036786556243896 -- g_loss: 0.5110732316970825 Epoch: 7 -- Step: 12520 -- d_loss: 1.348266839981079 -- g_loss: 0.4173773527145386 Epoch: 7 -- Step: 12530 -- d_loss: 1.1900911331176758 -- g_loss: 0.9925256967544556 Epoch: 7 -- Step: 12540 -- d_loss: 1.145676612854004 -- g_loss: 0.8847962617874146 Epoch: 7 -- Step: 12550 -- d_loss: 1.2016080617904663 -- g_loss: 0.6112751960754395 Epoch: 7 -- Step: 12560 -- d_loss: 0.7877948880195618 -- g_loss: 0.8571693897247314 Epoch: 7 -- Step: 12570 -- d_loss: 1.0235437154769897 -- g_loss: 0.6720038652420044 Epoch: 7 -- Step: 12580 -- d_loss: 0.9548826217651367 -- g_loss: 1.0626699924468994 Epoch: 7 -- Step: 12590 -- d_loss: 1.5863507986068726 -- g_loss: 0.33721596002578735 Epoch: 7 -- Step: 12600 -- d_loss: 1.2921149730682373 -- g_loss: 1.2531871795654297
Epoch: 7 -- Step: 12610 -- d_loss: 1.1396149396896362 -- g_loss: 0.5998074412345886 Epoch: 7 -- Step: 12620 -- d_loss: 1.0805811882019043 -- g_loss: 0.7159701585769653 Epoch: 7 -- Step: 12630 -- d_loss: 0.974211573600769 -- g_loss: 0.846624493598938 Epoch: 7 -- Step: 12640 -- d_loss: 0.7902294397354126 -- g_loss: 1.917959213256836 Epoch: 7 -- Step: 12650 -- d_loss: 1.1891896724700928 -- g_loss: 0.6265522837638855 Epoch: 8 -- Step: 12660 -- d_loss: 1.102083683013916 -- g_loss: 0.5777502059936523 Epoch: 8 -- Step: 12670 -- d_loss: 1.27796471118927 -- g_loss: 0.49178922176361084 Epoch: 8 -- Step: 12680 -- d_loss: 1.1512302160263062 -- g_loss: 0.9207201600074768 Epoch: 8 -- Step: 12690 -- d_loss: 1.4066612720489502 -- g_loss: 1.292271614074707 Epoch: 8 -- Step: 12700 -- d_loss: 0.9546627402305603 -- g_loss: 0.8096730709075928
Epoch: 8 -- Step: 12710 -- d_loss: 0.9612985253334045 -- g_loss: 1.0252301692962646 Epoch: 8 -- Step: 12720 -- d_loss: 0.8791574835777283 -- g_loss: 1.0395574569702148 Epoch: 8 -- Step: 12730 -- d_loss: 1.034727692604065 -- g_loss: 0.6693248748779297 Epoch: 8 -- Step: 12740 -- d_loss: 1.3616414070129395 -- g_loss: 0.41340863704681396 Epoch: 8 -- Step: 12750 -- d_loss: 0.7622731924057007 -- g_loss: 1.1747472286224365 Epoch: 8 -- Step: 12760 -- d_loss: 0.6119101047515869 -- g_loss: 1.2706782817840576 Epoch: 8 -- Step: 12770 -- d_loss: 0.9215384125709534 -- g_loss: 0.8880575299263 Epoch: 8 -- Step: 12780 -- d_loss: 1.361535906791687 -- g_loss: 0.4223721921443939 Epoch: 8 -- Step: 12790 -- d_loss: 1.3657028675079346 -- g_loss: 0.3941657543182373 Epoch: 8 -- Step: 12800 -- d_loss: 1.1809824705123901 -- g_loss: 0.6180216073989868
Epoch: 8 -- Step: 12810 -- d_loss: 0.8847187757492065 -- g_loss: 1.0728013515472412 Epoch: 8 -- Step: 12820 -- d_loss: 0.916312038898468 -- g_loss: 0.972099781036377 Epoch: 8 -- Step: 12830 -- d_loss: 0.8757873773574829 -- g_loss: 1.651344656944275 Epoch: 8 -- Step: 12840 -- d_loss: 1.5248231887817383 -- g_loss: 0.3414245843887329 Epoch: 8 -- Step: 12850 -- d_loss: 1.4036471843719482 -- g_loss: 0.41217511892318726 Epoch: 8 -- Step: 12860 -- d_loss: 1.0934906005859375 -- g_loss: 1.0806725025177002 Epoch: 8 -- Step: 12870 -- d_loss: 0.9989306926727295 -- g_loss: 0.9148115515708923 Epoch: 8 -- Step: 12880 -- d_loss: 1.683258056640625 -- g_loss: 0.35995328426361084 Epoch: 8 -- Step: 12890 -- d_loss: 0.9905226230621338 -- g_loss: 0.8720383644104004 Epoch: 8 -- Step: 12900 -- d_loss: 1.0104717016220093 -- g_loss: 0.7972028255462646
Epoch: 8 -- Step: 12910 -- d_loss: 1.2490284442901611 -- g_loss: 0.636626124382019 Epoch: 8 -- Step: 12920 -- d_loss: 0.8028497695922852 -- g_loss: 1.0954240560531616 Epoch: 8 -- Step: 12930 -- d_loss: 0.8275530338287354 -- g_loss: 1.0513652563095093 Epoch: 8 -- Step: 12940 -- d_loss: 1.4554541110992432 -- g_loss: 0.3741460144519806 Epoch: 8 -- Step: 12950 -- d_loss: 0.618116021156311 -- g_loss: 1.6207640171051025 Epoch: 8 -- Step: 12960 -- d_loss: 1.4119842052459717 -- g_loss: 0.4160040616989136 Epoch: 8 -- Step: 12970 -- d_loss: 1.1031608581542969 -- g_loss: 0.5653397440910339 Epoch: 8 -- Step: 12980 -- d_loss: 0.9528390169143677 -- g_loss: 0.8414144515991211 Epoch: 8 -- Step: 12990 -- d_loss: 1.1863926649093628 -- g_loss: 0.6134178638458252 Epoch: 8 -- Step: 13000 -- d_loss: 1.098632574081421 -- g_loss: 0.7681753039360046
Epoch: 8 -- Step: 13010 -- d_loss: 0.689390242099762 -- g_loss: 1.287010669708252 Epoch: 8 -- Step: 13020 -- d_loss: 1.7196264266967773 -- g_loss: 0.3004589080810547 Epoch: 8 -- Step: 13030 -- d_loss: 1.0921863317489624 -- g_loss: 0.6663200855255127 Epoch: 8 -- Step: 13040 -- d_loss: 1.091064453125 -- g_loss: 0.6591607332229614 Epoch: 8 -- Step: 13050 -- d_loss: 1.143558382987976 -- g_loss: 1.261460781097412 Epoch: 8 -- Step: 13060 -- d_loss: 1.1131993532180786 -- g_loss: 0.8023656010627747 Epoch: 8 -- Step: 13070 -- d_loss: 1.2336699962615967 -- g_loss: 0.5257002711296082 Epoch: 8 -- Step: 13080 -- d_loss: 0.940981388092041 -- g_loss: 0.729280948638916 Epoch: 8 -- Step: 13090 -- d_loss: 0.622725248336792 -- g_loss: 1.4032974243164062 Epoch: 8 -- Step: 13100 -- d_loss: 0.9186568260192871 -- g_loss: 0.8330819010734558
Epoch: 8 -- Step: 13110 -- d_loss: 0.7779073715209961 -- g_loss: 0.8851908445358276 Epoch: 8 -- Step: 13120 -- d_loss: 1.1560322046279907 -- g_loss: 0.66145920753479 Epoch: 8 -- Step: 13130 -- d_loss: 1.190955638885498 -- g_loss: 1.308767557144165 Epoch: 8 -- Step: 13140 -- d_loss: 0.7258566617965698 -- g_loss: 1.071494698524475 Epoch: 8 -- Step: 13150 -- d_loss: 1.472899317741394 -- g_loss: 0.39346015453338623 Epoch: 8 -- Step: 13160 -- d_loss: 1.0913174152374268 -- g_loss: 0.8265706300735474 Epoch: 8 -- Step: 13170 -- d_loss: 1.4218521118164062 -- g_loss: 0.46643245220184326 Epoch: 8 -- Step: 13180 -- d_loss: 0.9745303392410278 -- g_loss: 0.8480583429336548 Epoch: 8 -- Step: 13190 -- d_loss: 1.6618520021438599 -- g_loss: 0.306727796792984 Epoch: 8 -- Step: 13200 -- d_loss: 1.0179826021194458 -- g_loss: 0.813405454158783
Epoch: 8 -- Step: 13210 -- d_loss: 1.3461107015609741 -- g_loss: 0.42812466621398926 Epoch: 8 -- Step: 13220 -- d_loss: 1.149179458618164 -- g_loss: 1.0105679035186768 Epoch: 8 -- Step: 13230 -- d_loss: 1.1547024250030518 -- g_loss: 0.8713415265083313 Epoch: 8 -- Step: 13240 -- d_loss: 1.501924753189087 -- g_loss: 0.37471187114715576 Epoch: 8 -- Step: 13250 -- d_loss: 0.9769037961959839 -- g_loss: 1.108728289604187 Epoch: 8 -- Step: 13260 -- d_loss: 1.095000982284546 -- g_loss: 0.600405216217041 Epoch: 8 -- Step: 13270 -- d_loss: 1.2398550510406494 -- g_loss: 0.562829852104187 Epoch: 8 -- Step: 13280 -- d_loss: 0.9370989203453064 -- g_loss: 0.7613645792007446 Epoch: 8 -- Step: 13290 -- d_loss: 1.951766014099121 -- g_loss: 0.24161961674690247 Epoch: 8 -- Step: 13300 -- d_loss: 1.052658200263977 -- g_loss: 0.6308696269989014
Epoch: 8 -- Step: 13310 -- d_loss: 0.842781662940979 -- g_loss: 1.0309008359909058 Epoch: 8 -- Step: 13320 -- d_loss: 0.9213454127311707 -- g_loss: 1.748746633529663 Epoch: 8 -- Step: 13330 -- d_loss: 0.857024073600769 -- g_loss: 0.8878856301307678 Epoch: 8 -- Step: 13340 -- d_loss: 1.6086732149124146 -- g_loss: 1.9538143873214722 Epoch: 8 -- Step: 13350 -- d_loss: 2.013183832168579 -- g_loss: 0.8831573724746704 Epoch: 8 -- Step: 13360 -- d_loss: 1.167704463005066 -- g_loss: 0.6203535199165344 Epoch: 8 -- Step: 13370 -- d_loss: 1.0087071657180786 -- g_loss: 0.8999236822128296 Epoch: 8 -- Step: 13380 -- d_loss: 0.954098641872406 -- g_loss: 0.95665043592453 Epoch: 8 -- Step: 13390 -- d_loss: 1.0212225914001465 -- g_loss: 0.7734150290489197 Epoch: 8 -- Step: 13400 -- d_loss: 0.9904624223709106 -- g_loss: 0.9244011640548706
Epoch: 8 -- Step: 13410 -- d_loss: 0.8982762098312378 -- g_loss: 0.9303317666053772 Epoch: 8 -- Step: 13420 -- d_loss: 1.2065300941467285 -- g_loss: 0.6116834878921509 Epoch: 8 -- Step: 13430 -- d_loss: 1.0051076412200928 -- g_loss: 0.7786106467247009 Epoch: 8 -- Step: 13440 -- d_loss: 0.8641045093536377 -- g_loss: 0.9741228818893433 Epoch: 8 -- Step: 13450 -- d_loss: 1.229209065437317 -- g_loss: 0.6283933520317078 Epoch: 8 -- Step: 13460 -- d_loss: 1.046781063079834 -- g_loss: 0.8195458054542542 Epoch: 8 -- Step: 13470 -- d_loss: 1.1807082891464233 -- g_loss: 0.5317832827568054 Epoch: 8 -- Step: 13480 -- d_loss: 1.5383955240249634 -- g_loss: 0.42362716794013977 Epoch: 8 -- Step: 13490 -- d_loss: 1.5489474534988403 -- g_loss: 0.3775220513343811 Epoch: 8 -- Step: 13500 -- d_loss: 1.225778579711914 -- g_loss: 0.5702704787254333
Epoch: 8 -- Step: 13510 -- d_loss: 0.9354271292686462 -- g_loss: 0.7797408699989319 Epoch: 8 -- Step: 13520 -- d_loss: 1.1818702220916748 -- g_loss: 0.6402146220207214 Epoch: 8 -- Step: 13530 -- d_loss: 0.7745317816734314 -- g_loss: 1.083754539489746 Epoch: 8 -- Step: 13540 -- d_loss: 1.1237821578979492 -- g_loss: 0.6143823862075806 Epoch: 8 -- Step: 13550 -- d_loss: 1.6164790391921997 -- g_loss: 0.3768826127052307 Epoch: 8 -- Step: 13560 -- d_loss: 1.0594756603240967 -- g_loss: 1.0252912044525146 Epoch: 8 -- Step: 13570 -- d_loss: 0.9982845783233643 -- g_loss: 0.7280139327049255 Epoch: 8 -- Step: 13580 -- d_loss: 0.9601963758468628 -- g_loss: 0.9145151376724243 Epoch: 8 -- Step: 13590 -- d_loss: 1.661439299583435 -- g_loss: 0.28724759817123413 Epoch: 8 -- Step: 13600 -- d_loss: 1.319855809211731 -- g_loss: 0.4423786401748657
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Epoch: 9 -- Step: 15210 -- d_loss: 1.305774211883545 -- g_loss: 0.5064210891723633 Epoch: 9 -- Step: 15220 -- d_loss: 1.3384886980056763 -- g_loss: 0.4735923707485199 Epoch: 9 -- Step: 15230 -- d_loss: 1.4458709955215454 -- g_loss: 0.389423131942749 Epoch: 9 -- Step: 15240 -- d_loss: 1.1175029277801514 -- g_loss: 0.7551906108856201 Epoch: 9 -- Step: 15250 -- d_loss: 0.8838955163955688 -- g_loss: 0.8610376119613647 Epoch: 9 -- Step: 15260 -- d_loss: 1.0685091018676758 -- g_loss: 0.9452285766601562 Epoch: 9 -- Step: 15270 -- d_loss: 0.9366645812988281 -- g_loss: 0.9472772479057312 Epoch: 9 -- Step: 15280 -- d_loss: 0.598818302154541 -- g_loss: 1.5529649257659912 Epoch: 9 -- Step: 15290 -- d_loss: 1.1829109191894531 -- g_loss: 0.5615927577018738 Epoch: 9 -- Step: 15300 -- d_loss: 1.255449891090393 -- g_loss: 0.6073980331420898
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Epoch: 9 -- Step: 15810 -- d_loss: 1.2343791723251343 -- g_loss: 0.515920877456665 Epoch: 9 -- Step: 15820 -- d_loss: 1.3007071018218994 -- g_loss: 0.4594666659832001 Epoch: 10 -- Step: 15830 -- d_loss: 1.353212833404541 -- g_loss: 0.483630895614624 Epoch: 10 -- Step: 15840 -- d_loss: 0.9244747757911682 -- g_loss: 0.833836019039154 Epoch: 10 -- Step: 15850 -- d_loss: 0.721591591835022 -- g_loss: 1.8004428148269653 Epoch: 10 -- Step: 15860 -- d_loss: 0.6800620555877686 -- g_loss: 1.8677421808242798 Epoch: 10 -- Step: 15870 -- d_loss: 1.452378511428833 -- g_loss: 0.5767654180526733 Epoch: 10 -- Step: 15880 -- d_loss: 1.300910234451294 -- g_loss: 0.4510827362537384 Epoch: 10 -- Step: 15890 -- d_loss: 1.0118238925933838 -- g_loss: 0.7305471897125244 Epoch: 10 -- Step: 15900 -- d_loss: 0.7836172580718994 -- g_loss: 1.0966010093688965
Epoch: 10 -- Step: 15910 -- d_loss: 1.3036048412322998 -- g_loss: 0.46337181329727173 Epoch: 10 -- Step: 15920 -- d_loss: 1.112854242324829 -- g_loss: 0.9984589219093323 Epoch: 10 -- Step: 15930 -- d_loss: 1.0423665046691895 -- g_loss: 0.9398630261421204 Epoch: 10 -- Step: 15940 -- d_loss: 0.9886466264724731 -- g_loss: 0.7300869226455688 Epoch: 10 -- Step: 15950 -- d_loss: 0.6124560236930847 -- g_loss: 2.4890310764312744 Epoch: 10 -- Step: 15960 -- d_loss: 1.2367806434631348 -- g_loss: 1.1703912019729614 Epoch: 10 -- Step: 15970 -- d_loss: 1.2649226188659668 -- g_loss: 0.47349873185157776 Epoch: 10 -- Step: 15980 -- d_loss: 1.2118098735809326 -- g_loss: 0.6749159097671509 Epoch: 10 -- Step: 15990 -- d_loss: 1.298503041267395 -- g_loss: 0.47230595350265503 Epoch: 10 -- Step: 16000 -- d_loss: 0.959160327911377 -- g_loss: 0.9048249125480652
Epoch: 10 -- Step: 16010 -- d_loss: 0.9118121862411499 -- g_loss: 0.6847065091133118 Epoch: 10 -- Step: 16020 -- d_loss: 0.9358516335487366 -- g_loss: 0.774325966835022 Epoch: 10 -- Step: 16030 -- d_loss: 1.0818836688995361 -- g_loss: 0.6537225246429443 Epoch: 10 -- Step: 16040 -- d_loss: 1.2266467809677124 -- g_loss: 0.5395098328590393 Epoch: 10 -- Step: 16050 -- d_loss: 1.2113971710205078 -- g_loss: 0.4730788469314575 Epoch: 10 -- Step: 16060 -- d_loss: 1.5778274536132812 -- g_loss: 0.33158084750175476 Epoch: 10 -- Step: 16070 -- d_loss: 1.304734468460083 -- g_loss: 0.483366459608078 Epoch: 10 -- Step: 16080 -- d_loss: 0.8379734754562378 -- g_loss: 0.9036083817481995 Epoch: 10 -- Step: 16090 -- d_loss: 1.02284836769104 -- g_loss: 0.6018635034561157 Epoch: 10 -- Step: 16100 -- d_loss: 0.8948090076446533 -- g_loss: 0.9265180826187134
Epoch: 10 -- Step: 16110 -- d_loss: 0.9066752195358276 -- g_loss: 0.7459521293640137 Epoch: 10 -- Step: 16120 -- d_loss: 0.8872382044792175 -- g_loss: 0.8503015041351318 Epoch: 10 -- Step: 16130 -- d_loss: 0.6777387857437134 -- g_loss: 1.1082640886306763 Epoch: 10 -- Step: 16140 -- d_loss: 1.0585812330245972 -- g_loss: 2.0296974182128906 Epoch: 10 -- Step: 16150 -- d_loss: 1.1072160005569458 -- g_loss: 0.7057327032089233 Epoch: 10 -- Step: 16160 -- d_loss: 1.1244556903839111 -- g_loss: 0.8501745462417603 Epoch: 10 -- Step: 16170 -- d_loss: 5.805746555328369 -- g_loss: 0.011323302052915096 Epoch: 10 -- Step: 16180 -- d_loss: 1.301612138748169 -- g_loss: 0.5764567852020264 Epoch: 10 -- Step: 16190 -- d_loss: 1.2132737636566162 -- g_loss: 0.6570132970809937 Epoch: 10 -- Step: 16200 -- d_loss: 0.7748028039932251 -- g_loss: 0.8358608484268188
Epoch: 10 -- Step: 16210 -- d_loss: 1.128004789352417 -- g_loss: 0.600344181060791 Epoch: 10 -- Step: 16220 -- d_loss: 1.5319979190826416 -- g_loss: 0.353272020816803 Epoch: 10 -- Step: 16230 -- d_loss: 1.1972119808197021 -- g_loss: 0.6273722648620605 Epoch: 10 -- Step: 16240 -- d_loss: 1.172675371170044 -- g_loss: 0.8967376947402954 Epoch: 10 -- Step: 16250 -- d_loss: 0.8343408107757568 -- g_loss: 1.0333178043365479 Epoch: 10 -- Step: 16260 -- d_loss: 1.2294374704360962 -- g_loss: 0.5668455958366394 Epoch: 10 -- Step: 16270 -- d_loss: 1.0382235050201416 -- g_loss: 0.7630407214164734 Epoch: 10 -- Step: 16280 -- d_loss: 1.0061485767364502 -- g_loss: 0.9811535477638245 Epoch: 10 -- Step: 16290 -- d_loss: 0.7992646098136902 -- g_loss: 0.8373798727989197 Epoch: 10 -- Step: 16300 -- d_loss: 1.0743777751922607 -- g_loss: 0.7382012009620667
Epoch: 10 -- Step: 16310 -- d_loss: 0.8663949966430664 -- g_loss: 0.9047608375549316 Epoch: 10 -- Step: 16320 -- d_loss: 1.1086777448654175 -- g_loss: 0.6097594499588013 Epoch: 10 -- Step: 16330 -- d_loss: 1.0795273780822754 -- g_loss: 0.9644957184791565 Epoch: 10 -- Step: 16340 -- d_loss: 1.1624892950057983 -- g_loss: 0.5968626737594604 Epoch: 10 -- Step: 16350 -- d_loss: 1.2752470970153809 -- g_loss: 0.4204949140548706 Epoch: 10 -- Step: 16360 -- d_loss: 0.8527217507362366 -- g_loss: 0.7863186597824097 Epoch: 10 -- Step: 16370 -- d_loss: 1.0701780319213867 -- g_loss: 0.961726188659668 Epoch: 10 -- Step: 16380 -- d_loss: 0.8493777513504028 -- g_loss: 0.9149503707885742 Epoch: 10 -- Step: 16390 -- d_loss: 1.167893886566162 -- g_loss: 0.5105385184288025 Epoch: 10 -- Step: 16400 -- d_loss: 1.1190751791000366 -- g_loss: 0.5956509113311768
Epoch: 10 -- Step: 16410 -- d_loss: 0.9752451777458191 -- g_loss: 0.7230768203735352 Epoch: 10 -- Step: 16420 -- d_loss: 1.554370403289795 -- g_loss: 0.34534966945648193 Epoch: 10 -- Step: 16430 -- d_loss: 0.8902907371520996 -- g_loss: 0.8629288673400879 Epoch: 10 -- Step: 16440 -- d_loss: 1.0031373500823975 -- g_loss: 1.0204074382781982 Epoch: 10 -- Step: 16450 -- d_loss: 0.8492034077644348 -- g_loss: 1.5199800729751587 Epoch: 10 -- Step: 16460 -- d_loss: 1.5984841585159302 -- g_loss: 0.33759140968322754 Epoch: 10 -- Step: 16470 -- d_loss: 1.0755767822265625 -- g_loss: 0.7629777193069458 Epoch: 10 -- Step: 16480 -- d_loss: 1.2172826528549194 -- g_loss: 0.6249732971191406 Epoch: 10 -- Step: 16490 -- d_loss: 1.4198154211044312 -- g_loss: 0.4271201491355896 Epoch: 10 -- Step: 16500 -- d_loss: 1.212519884109497 -- g_loss: 0.5859754085540771
Epoch: 10 -- Step: 16510 -- d_loss: 1.5030441284179688 -- g_loss: 0.3876452147960663 Epoch: 10 -- Step: 16520 -- d_loss: 1.2686431407928467 -- g_loss: 0.48070281744003296 Epoch: 10 -- Step: 16530 -- d_loss: 1.2936636209487915 -- g_loss: 0.4835124909877777 Epoch: 10 -- Step: 16540 -- d_loss: 0.8956059217453003 -- g_loss: 0.9774675369262695 Epoch: 10 -- Step: 16550 -- d_loss: 1.7049751281738281 -- g_loss: 0.29050523042678833 Epoch: 10 -- Step: 16560 -- d_loss: 1.2517032623291016 -- g_loss: 0.5514727234840393 Epoch: 10 -- Step: 16570 -- d_loss: 1.0362650156021118 -- g_loss: 1.04119074344635 Epoch: 10 -- Step: 16580 -- d_loss: 0.6353418231010437 -- g_loss: 1.4042720794677734 Epoch: 10 -- Step: 16590 -- d_loss: 0.8517782688140869 -- g_loss: 0.888103723526001 Epoch: 10 -- Step: 16600 -- d_loss: 1.5606133937835693 -- g_loss: 0.3528580963611603
Epoch: 10 -- Step: 16610 -- d_loss: 0.9637395143508911 -- g_loss: 0.8234476447105408 Epoch: 10 -- Step: 16620 -- d_loss: 1.045701503753662 -- g_loss: 0.7340348958969116 Epoch: 10 -- Step: 16630 -- d_loss: 1.214003086090088 -- g_loss: 0.5236702561378479 Epoch: 10 -- Step: 16640 -- d_loss: 1.3324692249298096 -- g_loss: 0.42229074239730835 Epoch: 10 -- Step: 16650 -- d_loss: 0.7672669887542725 -- g_loss: 1.0479408502578735 Epoch: 10 -- Step: 16660 -- d_loss: 1.1545395851135254 -- g_loss: 0.5791305899620056 Epoch: 10 -- Step: 16670 -- d_loss: 0.9316653609275818 -- g_loss: 0.9136855602264404 Epoch: 10 -- Step: 16680 -- d_loss: 1.307637333869934 -- g_loss: 0.4677650034427643 Epoch: 10 -- Step: 16690 -- d_loss: 1.2370140552520752 -- g_loss: 0.7061368227005005 Epoch: 10 -- Step: 16700 -- d_loss: 0.7772005796432495 -- g_loss: 1.2458548545837402
Epoch: 10 -- Step: 16710 -- d_loss: 0.4369719624519348 -- g_loss: 2.2524917125701904 Epoch: 10 -- Step: 16720 -- d_loss: 1.0690429210662842 -- g_loss: 0.7852516174316406 Epoch: 10 -- Step: 16730 -- d_loss: 1.194749355316162 -- g_loss: 0.5978878736495972 Epoch: 10 -- Step: 16740 -- d_loss: 1.1951730251312256 -- g_loss: 0.5241636037826538 Epoch: 10 -- Step: 16750 -- d_loss: 1.0764235258102417 -- g_loss: 1.6636296510696411 Epoch: 10 -- Step: 16760 -- d_loss: 1.0805323123931885 -- g_loss: 0.9266269207000732 Epoch: 10 -- Step: 16770 -- d_loss: 1.0869165658950806 -- g_loss: 0.6005038619041443 Epoch: 10 -- Step: 16780 -- d_loss: 1.4741801023483276 -- g_loss: 0.36934348940849304 Epoch: 10 -- Step: 16790 -- d_loss: 0.8466256856918335 -- g_loss: 0.9214898347854614 Epoch: 10 -- Step: 16800 -- d_loss: 1.149228811264038 -- g_loss: 0.5845054388046265
Epoch: 10 -- Step: 16810 -- d_loss: 0.8952550888061523 -- g_loss: 0.8582909107208252 Epoch: 10 -- Step: 16820 -- d_loss: 1.0767529010772705 -- g_loss: 1.2290129661560059 Epoch: 10 -- Step: 16830 -- d_loss: 1.170362949371338 -- g_loss: 0.7143813371658325 Epoch: 10 -- Step: 16840 -- d_loss: 1.0840156078338623 -- g_loss: 0.7176851630210876 Epoch: 10 -- Step: 16850 -- d_loss: 0.8251621723175049 -- g_loss: 1.2801454067230225 Epoch: 10 -- Step: 16860 -- d_loss: 0.823052167892456 -- g_loss: 0.9730787873268127 Epoch: 10 -- Step: 16870 -- d_loss: 1.5689761638641357 -- g_loss: 0.4000459909439087 Epoch: 10 -- Step: 16880 -- d_loss: 1.4456989765167236 -- g_loss: 0.4298781752586365 Epoch: 10 -- Step: 16890 -- d_loss: 1.1148097515106201 -- g_loss: 0.7214211225509644 Epoch: 10 -- Step: 16900 -- d_loss: 1.076447606086731 -- g_loss: 1.3455626964569092
Epoch: 10 -- Step: 16910 -- d_loss: 1.1425669193267822 -- g_loss: 0.6457923650741577 Epoch: 10 -- Step: 16920 -- d_loss: 1.1172864437103271 -- g_loss: 0.663688063621521 Epoch: 10 -- Step: 16930 -- d_loss: 0.9120891094207764 -- g_loss: 1.1162197589874268 Epoch: 10 -- Step: 16940 -- d_loss: 0.9977953433990479 -- g_loss: 1.101916790008545 Epoch: 10 -- Step: 16950 -- d_loss: 1.2556525468826294 -- g_loss: 0.5249015092849731 Epoch: 10 -- Step: 16960 -- d_loss: 1.1206510066986084 -- g_loss: 0.7662581205368042 Epoch: 10 -- Step: 16970 -- d_loss: 0.9334680438041687 -- g_loss: 0.7456795573234558 Epoch: 10 -- Step: 16980 -- d_loss: 0.6837785840034485 -- g_loss: 1.128943920135498 Epoch: 10 -- Step: 16990 -- d_loss: 1.4253093004226685 -- g_loss: 1.091883897781372 Epoch: 10 -- Step: 17000 -- d_loss: 1.3738007545471191 -- g_loss: 0.46806591749191284
Epoch: 10 -- Step: 17010 -- d_loss: 1.053182601928711 -- g_loss: 1.0441884994506836 Epoch: 10 -- Step: 17020 -- d_loss: 0.8881951570510864 -- g_loss: 0.8086543679237366 Epoch: 10 -- Step: 17030 -- d_loss: 0.8950800895690918 -- g_loss: 0.9547922015190125 Epoch: 10 -- Step: 17040 -- d_loss: 0.9360087513923645 -- g_loss: 0.8539660573005676 Epoch: 10 -- Step: 17050 -- d_loss: 1.1506752967834473 -- g_loss: 0.8877525329589844 Epoch: 10 -- Step: 17060 -- d_loss: 1.148026943206787 -- g_loss: 0.7432541847229004 Epoch: 10 -- Step: 17070 -- d_loss: 0.8217010498046875 -- g_loss: 0.7841799855232239 Epoch: 10 -- Step: 17080 -- d_loss: 0.7353591322898865 -- g_loss: 1.38340425491333 Epoch: 10 -- Step: 17090 -- d_loss: 1.357397437095642 -- g_loss: 0.4437989592552185 Epoch: 10 -- Step: 17100 -- d_loss: 0.9064391851425171 -- g_loss: 0.8129713535308838
Epoch: 10 -- Step: 17110 -- d_loss: 1.1176354885101318 -- g_loss: 0.9669196605682373 Epoch: 10 -- Step: 17120 -- d_loss: 1.1229569911956787 -- g_loss: 0.6392186880111694 Epoch: 10 -- Step: 17130 -- d_loss: 1.0766503810882568 -- g_loss: 0.6224976778030396 Epoch: 10 -- Step: 17140 -- d_loss: 1.1760584115982056 -- g_loss: 1.3049650192260742 Epoch: 10 -- Step: 17150 -- d_loss: 1.065923810005188 -- g_loss: 0.6682091951370239 Epoch: 10 -- Step: 17160 -- d_loss: 0.890965461730957 -- g_loss: 0.8343761563301086 Epoch: 10 -- Step: 17170 -- d_loss: 1.1954373121261597 -- g_loss: 0.6525123119354248 Epoch: 10 -- Step: 17180 -- d_loss: 1.3738460540771484 -- g_loss: 0.4664192795753479 Epoch: 10 -- Step: 17190 -- d_loss: 0.5712151527404785 -- g_loss: 2.0613362789154053 Epoch: 10 -- Step: 17200 -- d_loss: 0.9271530508995056 -- g_loss: 0.7941020727157593
Epoch: 10 -- Step: 17210 -- d_loss: 0.8595551252365112 -- g_loss: 1.1336638927459717 Epoch: 10 -- Step: 17220 -- d_loss: 0.8809203505516052 -- g_loss: 0.8107429146766663 Epoch: 10 -- Step: 17230 -- d_loss: 0.7961724996566772 -- g_loss: 2.460613250732422 Epoch: 10 -- Step: 17240 -- d_loss: 1.0216373205184937 -- g_loss: 0.6795297861099243 Epoch: 10 -- Step: 17250 -- d_loss: 1.1036953926086426 -- g_loss: 1.337927222251892 Epoch: 10 -- Step: 17260 -- d_loss: 1.2203933000564575 -- g_loss: 0.5875149965286255 Epoch: 10 -- Step: 17270 -- d_loss: 0.9227688312530518 -- g_loss: 0.7465683817863464 Epoch: 10 -- Step: 17280 -- d_loss: 1.4670112133026123 -- g_loss: 0.40326792001724243 Epoch: 10 -- Step: 17290 -- d_loss: 0.7658001184463501 -- g_loss: 1.2856496572494507 Epoch: 10 -- Step: 17300 -- d_loss: 1.293251633644104 -- g_loss: 0.453346848487854
Epoch: 10 -- Step: 17310 -- d_loss: 1.3532967567443848 -- g_loss: 0.4351429045200348 Epoch: 10 -- Step: 17320 -- d_loss: 1.1286956071853638 -- g_loss: 0.6905109286308289 Epoch: 10 -- Step: 17330 -- d_loss: 1.0155439376831055 -- g_loss: 0.9578272104263306 Epoch: 10 -- Step: 17340 -- d_loss: 1.6155383586883545 -- g_loss: 0.3730723261833191 Epoch: 10 -- Step: 17350 -- d_loss: 0.9743300676345825 -- g_loss: 0.9260827898979187 Epoch: 10 -- Step: 17360 -- d_loss: 1.2128510475158691 -- g_loss: 0.5553600788116455 Epoch: 10 -- Step: 17370 -- d_loss: 1.0005693435668945 -- g_loss: 0.8418582677841187 Epoch: 10 -- Step: 17380 -- d_loss: 1.0410079956054688 -- g_loss: 0.7225794792175293 Epoch: 10 -- Step: 17390 -- d_loss: 1.1597657203674316 -- g_loss: 0.8327885866165161 Epoch: 10 -- Step: 17400 -- d_loss: 1.3703910112380981 -- g_loss: 0.42073678970336914
Epoch: 11 -- Step: 17410 -- d_loss: 1.1282958984375 -- g_loss: 0.6285308599472046 Epoch: 11 -- Step: 17420 -- d_loss: 1.1453323364257812 -- g_loss: 0.6391606330871582 Epoch: 11 -- Step: 17430 -- d_loss: 1.0683300495147705 -- g_loss: 0.6752637624740601 Epoch: 11 -- Step: 17440 -- d_loss: 1.2507102489471436 -- g_loss: 0.5182585120201111 Epoch: 11 -- Step: 17450 -- d_loss: 1.1058295965194702 -- g_loss: 0.5617599487304688 Epoch: 11 -- Step: 17460 -- d_loss: 1.0056326389312744 -- g_loss: 0.6291072368621826 Epoch: 11 -- Step: 17470 -- d_loss: 3.0197958946228027 -- g_loss: 2.157222032546997 Epoch: 11 -- Step: 17480 -- d_loss: 1.5894112586975098 -- g_loss: 0.9990940093994141 Epoch: 11 -- Step: 17490 -- d_loss: 1.2936162948608398 -- g_loss: 0.5270377993583679 Epoch: 11 -- Step: 17500 -- d_loss: 1.0905896425247192 -- g_loss: 0.7909878492355347
Epoch: 11 -- Step: 17510 -- d_loss: 1.2209948301315308 -- g_loss: 0.6388038396835327 Epoch: 11 -- Step: 17520 -- d_loss: 0.9739535450935364 -- g_loss: 0.7920228838920593 Epoch: 11 -- Step: 17530 -- d_loss: 0.9729436635971069 -- g_loss: 0.7206840515136719 Epoch: 11 -- Step: 17540 -- d_loss: 0.7704065442085266 -- g_loss: 1.0880805253982544 Epoch: 11 -- Step: 17550 -- d_loss: 0.9039362668991089 -- g_loss: 0.8204400539398193 Epoch: 11 -- Step: 17560 -- d_loss: 1.4901459217071533 -- g_loss: 0.4986686110496521 Epoch: 11 -- Step: 17570 -- d_loss: 1.2829585075378418 -- g_loss: 0.5061498284339905 Epoch: 11 -- Step: 17580 -- d_loss: 1.1394364833831787 -- g_loss: 0.8717118501663208 Epoch: 11 -- Step: 17590 -- d_loss: 0.9551740884780884 -- g_loss: 0.8650432229042053 Epoch: 11 -- Step: 17600 -- d_loss: 1.0335694551467896 -- g_loss: 0.7514119148254395
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Epoch: 11 -- Step: 17710 -- d_loss: 0.7467144727706909 -- g_loss: 1.295730471611023 Epoch: 11 -- Step: 17720 -- d_loss: 0.8980165123939514 -- g_loss: 0.9262170791625977 Epoch: 11 -- Step: 17730 -- d_loss: 0.9024844765663147 -- g_loss: 0.8482491970062256 Epoch: 11 -- Step: 17740 -- d_loss: 1.0962125062942505 -- g_loss: 0.6369861960411072 Epoch: 11 -- Step: 17750 -- d_loss: 1.0651516914367676 -- g_loss: 0.8134530186653137 Epoch: 11 -- Step: 17760 -- d_loss: 1.0728167295455933 -- g_loss: 0.6171832084655762 Epoch: 11 -- Step: 17770 -- d_loss: 1.474308729171753 -- g_loss: 0.4253031611442566 Epoch: 11 -- Step: 17780 -- d_loss: 1.0990684032440186 -- g_loss: 0.8107079267501831 Epoch: 11 -- Step: 17790 -- d_loss: 0.5359983444213867 -- g_loss: 1.7577855587005615 Epoch: 11 -- Step: 17800 -- d_loss: 0.9278900027275085 -- g_loss: 0.9105473160743713
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Epoch: 11 -- Step: 18510 -- d_loss: 0.9383971691131592 -- g_loss: 0.9148961901664734 Epoch: 11 -- Step: 18520 -- d_loss: 0.9626244306564331 -- g_loss: 1.1893101930618286 Epoch: 11 -- Step: 18530 -- d_loss: 1.4389913082122803 -- g_loss: 0.36893415451049805 Epoch: 11 -- Step: 18540 -- d_loss: 0.768295168876648 -- g_loss: 1.2605979442596436 Epoch: 11 -- Step: 18550 -- d_loss: 1.1333587169647217 -- g_loss: 0.6707850694656372 Epoch: 11 -- Step: 18560 -- d_loss: 0.8842763304710388 -- g_loss: 0.9760730862617493 Epoch: 11 -- Step: 18570 -- d_loss: 1.2035014629364014 -- g_loss: 0.8017750978469849 Epoch: 11 -- Step: 18580 -- d_loss: 1.015920877456665 -- g_loss: 0.8127403259277344 Epoch: 11 -- Step: 18590 -- d_loss: 1.1740251779556274 -- g_loss: 0.5906649827957153 Epoch: 11 -- Step: 18600 -- d_loss: 1.0456690788269043 -- g_loss: 0.782257080078125
Epoch: 11 -- Step: 18610 -- d_loss: 1.4215576648712158 -- g_loss: 0.38434481620788574 Epoch: 11 -- Step: 18620 -- d_loss: 1.2276791334152222 -- g_loss: 0.4809061288833618 Epoch: 11 -- Step: 18630 -- d_loss: 0.9493484497070312 -- g_loss: 0.9217523336410522 Epoch: 11 -- Step: 18640 -- d_loss: 0.8302546739578247 -- g_loss: 1.0944528579711914 Epoch: 11 -- Step: 18650 -- d_loss: 1.2573070526123047 -- g_loss: 0.5049769878387451 Epoch: 11 -- Step: 18660 -- d_loss: 1.2667306661605835 -- g_loss: 0.503771185874939 Epoch: 11 -- Step: 18670 -- d_loss: 1.02207350730896 -- g_loss: 1.0827569961547852 Epoch: 11 -- Step: 18680 -- d_loss: 0.7149192094802856 -- g_loss: 1.3488080501556396 Epoch: 11 -- Step: 18690 -- d_loss: 0.6995642185211182 -- g_loss: 1.153823971748352 Epoch: 11 -- Step: 18700 -- d_loss: 0.8796994090080261 -- g_loss: 1.0764697790145874
Epoch: 11 -- Step: 18710 -- d_loss: 1.2963494062423706 -- g_loss: 0.49487876892089844 Epoch: 11 -- Step: 18720 -- d_loss: 0.9308723211288452 -- g_loss: 0.9965156316757202 Epoch: 11 -- Step: 18730 -- d_loss: 0.7538131475448608 -- g_loss: 1.2729660272598267 Epoch: 11 -- Step: 18740 -- d_loss: 1.0739655494689941 -- g_loss: 0.6119025349617004 Epoch: 11 -- Step: 18750 -- d_loss: 1.4195162057876587 -- g_loss: 0.3903609812259674 Epoch: 11 -- Step: 18760 -- d_loss: 0.9069509506225586 -- g_loss: 1.2316875457763672 Epoch: 11 -- Step: 18770 -- d_loss: 1.252455472946167 -- g_loss: 0.49340829253196716 Epoch: 11 -- Step: 18780 -- d_loss: 1.1233282089233398 -- g_loss: 0.5944230556488037 Epoch: 11 -- Step: 18790 -- d_loss: 1.3727235794067383 -- g_loss: 0.4231291115283966 Epoch: 11 -- Step: 18800 -- d_loss: 1.2077457904815674 -- g_loss: 0.6970786452293396
Epoch: 11 -- Step: 18810 -- d_loss: 1.4509060382843018 -- g_loss: 0.39645251631736755 Epoch: 11 -- Step: 18820 -- d_loss: 1.0456374883651733 -- g_loss: 0.6242221593856812 Epoch: 11 -- Step: 18830 -- d_loss: 1.1708569526672363 -- g_loss: 0.7083358764648438 Epoch: 11 -- Step: 18840 -- d_loss: 1.1229400634765625 -- g_loss: 0.9108433127403259 Epoch: 11 -- Step: 18850 -- d_loss: 1.3946013450622559 -- g_loss: 1.2493823766708374 Epoch: 11 -- Step: 18860 -- d_loss: 0.9588843584060669 -- g_loss: 0.714583158493042 Epoch: 11 -- Step: 18870 -- d_loss: 1.2714357376098633 -- g_loss: 0.5304762721061707 Epoch: 11 -- Step: 18880 -- d_loss: 1.508952260017395 -- g_loss: 0.33883798122406006 Epoch: 11 -- Step: 18890 -- d_loss: 1.0933287143707275 -- g_loss: 0.7672580480575562 Epoch: 11 -- Step: 18900 -- d_loss: 1.1039860248565674 -- g_loss: 0.7973601818084717
Epoch: 11 -- Step: 18910 -- d_loss: 1.1416707038879395 -- g_loss: 0.5994952321052551 Epoch: 11 -- Step: 18920 -- d_loss: 1.4215078353881836 -- g_loss: 0.4974589943885803 Epoch: 11 -- Step: 18930 -- d_loss: 0.9300053119659424 -- g_loss: 1.3268548250198364 Epoch: 11 -- Step: 18940 -- d_loss: 0.775977611541748 -- g_loss: 0.9475148916244507 Epoch: 11 -- Step: 18950 -- d_loss: 0.9635107517242432 -- g_loss: 0.8733474016189575 Epoch: 11 -- Step: 18960 -- d_loss: 1.1257367134094238 -- g_loss: 0.8178797960281372 Epoch: 11 -- Step: 18970 -- d_loss: 1.1555193662643433 -- g_loss: 1.6549004316329956 Epoch: 11 -- Step: 18980 -- d_loss: 1.1135753393173218 -- g_loss: 1.2434580326080322 Epoch: 12 -- Step: 18990 -- d_loss: 1.0924501419067383 -- g_loss: 0.6800224781036377 Epoch: 12 -- Step: 19000 -- d_loss: 0.9605216383934021 -- g_loss: 0.9837055206298828
Epoch: 12 -- Step: 19010 -- d_loss: 1.4719569683074951 -- g_loss: 0.4365508258342743 Epoch: 12 -- Step: 19020 -- d_loss: 1.2513550519943237 -- g_loss: 0.5323065519332886 Epoch: 12 -- Step: 19030 -- d_loss: 0.9888792037963867 -- g_loss: 0.694690465927124 Epoch: 12 -- Step: 19040 -- d_loss: 1.1680716276168823 -- g_loss: 0.6302215456962585 Epoch: 12 -- Step: 19050 -- d_loss: 0.8791837096214294 -- g_loss: 0.9861879348754883 Epoch: 12 -- Step: 19060 -- d_loss: 1.1872143745422363 -- g_loss: 0.5325376391410828 Epoch: 12 -- Step: 19070 -- d_loss: 1.3326503038406372 -- g_loss: 0.5503972768783569 Epoch: 12 -- Step: 19080 -- d_loss: 1.0980513095855713 -- g_loss: 0.9403333067893982 Epoch: 12 -- Step: 19090 -- d_loss: 1.2626457214355469 -- g_loss: 0.56915283203125 Epoch: 12 -- Step: 19100 -- d_loss: 1.052067518234253 -- g_loss: 0.7179424166679382
Epoch: 12 -- Step: 19110 -- d_loss: 1.2393364906311035 -- g_loss: 0.594485878944397 Epoch: 12 -- Step: 19120 -- d_loss: 1.076331377029419 -- g_loss: 1.1851521730422974 Epoch: 12 -- Step: 19130 -- d_loss: 1.2827417850494385 -- g_loss: 0.6249014139175415 Epoch: 12 -- Step: 19140 -- d_loss: 1.09578275680542 -- g_loss: 0.7640044689178467 Epoch: 12 -- Step: 19150 -- d_loss: 1.2882733345031738 -- g_loss: 1.8189890384674072 Epoch: 12 -- Step: 19160 -- d_loss: 0.8718900084495544 -- g_loss: 0.9503923058509827 Epoch: 12 -- Step: 19170 -- d_loss: 0.7670528888702393 -- g_loss: 1.098707675933838 Epoch: 12 -- Step: 19180 -- d_loss: 1.1080151796340942 -- g_loss: 0.6278394460678101 Epoch: 12 -- Step: 19190 -- d_loss: 0.9574662446975708 -- g_loss: 0.8771268725395203 Epoch: 12 -- Step: 19200 -- d_loss: 0.9673634767532349 -- g_loss: 0.7720410227775574
Epoch: 12 -- Step: 19210 -- d_loss: 0.985927939414978 -- g_loss: 1.2913310527801514 Epoch: 12 -- Step: 19220 -- d_loss: 1.1068220138549805 -- g_loss: 0.7102451920509338 Epoch: 12 -- Step: 19230 -- d_loss: 1.0952672958374023 -- g_loss: 0.8508473038673401 Epoch: 12 -- Step: 19240 -- d_loss: 1.0210459232330322 -- g_loss: 1.5166834592819214 Epoch: 12 -- Step: 19250 -- d_loss: 1.1719708442687988 -- g_loss: 0.5498238801956177 Epoch: 12 -- Step: 19260 -- d_loss: 1.1285096406936646 -- g_loss: 0.6857969760894775 Epoch: 12 -- Step: 19270 -- d_loss: 1.3988171815872192 -- g_loss: 0.4963056743144989 Epoch: 12 -- Step: 19280 -- d_loss: 1.3104631900787354 -- g_loss: 0.5826963186264038 Epoch: 12 -- Step: 19290 -- d_loss: 1.3042441606521606 -- g_loss: 0.5846654176712036 Epoch: 12 -- Step: 19300 -- d_loss: 1.140815019607544 -- g_loss: 1.2234385013580322
Epoch: 12 -- Step: 19310 -- d_loss: 0.766440212726593 -- g_loss: 1.155199408531189 Epoch: 12 -- Step: 19320 -- d_loss: 1.0123984813690186 -- g_loss: 0.7151648998260498 Epoch: 12 -- Step: 19330 -- d_loss: 1.1888445615768433 -- g_loss: 0.5854496955871582 Epoch: 12 -- Step: 19340 -- d_loss: 1.2098355293273926 -- g_loss: 0.6619240045547485 Epoch: 12 -- Step: 19350 -- d_loss: 0.9670757055282593 -- g_loss: 1.1979395151138306 Epoch: 12 -- Step: 19360 -- d_loss: 0.6295347213745117 -- g_loss: 1.4219872951507568 Epoch: 12 -- Step: 19370 -- d_loss: 1.1223466396331787 -- g_loss: 0.5467724800109863 Epoch: 12 -- Step: 19380 -- d_loss: 0.6719810366630554 -- g_loss: 1.075749397277832 Epoch: 12 -- Step: 19390 -- d_loss: 1.2777292728424072 -- g_loss: 0.4956502914428711 Epoch: 12 -- Step: 19400 -- d_loss: 1.1566845178604126 -- g_loss: 0.8335860371589661
Epoch: 12 -- Step: 19410 -- d_loss: 1.0898879766464233 -- g_loss: 0.6995816230773926 Epoch: 12 -- Step: 19420 -- d_loss: 0.7585432529449463 -- g_loss: 1.0755767822265625 Epoch: 12 -- Step: 19430 -- d_loss: 1.2134056091308594 -- g_loss: 0.5211421847343445 Epoch: 12 -- Step: 19440 -- d_loss: 1.4068214893341064 -- g_loss: 0.9524615406990051 Epoch: 12 -- Step: 19450 -- d_loss: 0.855114221572876 -- g_loss: 1.097182035446167 Epoch: 12 -- Step: 19460 -- d_loss: 1.042945384979248 -- g_loss: 0.6073460578918457 Epoch: 12 -- Step: 19470 -- d_loss: 1.0770832300186157 -- g_loss: 1.3304498195648193 Epoch: 12 -- Step: 19480 -- d_loss: 1.0187711715698242 -- g_loss: 0.675287127494812 Epoch: 12 -- Step: 19490 -- d_loss: 1.209165334701538 -- g_loss: 0.6464220285415649 Epoch: 12 -- Step: 19500 -- d_loss: 1.106520414352417 -- g_loss: 0.8374960422515869
Epoch: 12 -- Step: 19510 -- d_loss: 1.030729055404663 -- g_loss: 1.2134315967559814 Epoch: 12 -- Step: 19520 -- d_loss: 1.08700430393219 -- g_loss: 1.0883996486663818 Epoch: 12 -- Step: 19530 -- d_loss: 0.9153639078140259 -- g_loss: 0.7323526740074158 Epoch: 12 -- Step: 19540 -- d_loss: 1.0965547561645508 -- g_loss: 0.8469128012657166 Epoch: 12 -- Step: 19550 -- d_loss: 1.0204243659973145 -- g_loss: 0.6142439246177673 Epoch: 12 -- Step: 19560 -- d_loss: 1.0658323764801025 -- g_loss: 0.6990053057670593 Epoch: 12 -- Step: 19570 -- d_loss: 0.861825168132782 -- g_loss: 0.9893829822540283 Epoch: 12 -- Step: 19580 -- d_loss: 0.8823946714401245 -- g_loss: 0.8197308778762817 Epoch: 12 -- Step: 19590 -- d_loss: 0.9126911759376526 -- g_loss: 0.855139970779419 Epoch: 12 -- Step: 19600 -- d_loss: 1.1356868743896484 -- g_loss: 0.6913754940032959
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Epoch: 13 -- Step: 20910 -- d_loss: 1.438873291015625 -- g_loss: 0.49378612637519836 Epoch: 13 -- Step: 20920 -- d_loss: 0.8791592717170715 -- g_loss: 1.0661696195602417 Epoch: 13 -- Step: 20930 -- d_loss: 1.17958664894104 -- g_loss: 0.6209433674812317 Epoch: 13 -- Step: 20940 -- d_loss: 0.8744440078735352 -- g_loss: 0.8344502449035645 Epoch: 13 -- Step: 20950 -- d_loss: 1.0332305431365967 -- g_loss: 1.0298811197280884 Epoch: 13 -- Step: 20960 -- d_loss: 0.9196012020111084 -- g_loss: 0.8902766704559326 Epoch: 13 -- Step: 20970 -- d_loss: 1.1738888025283813 -- g_loss: 1.2197853326797485 Epoch: 13 -- Step: 20980 -- d_loss: 1.2393898963928223 -- g_loss: 0.476406991481781 Epoch: 13 -- Step: 20990 -- d_loss: 1.1359786987304688 -- g_loss: 0.6266674399375916 Epoch: 13 -- Step: 21000 -- d_loss: 1.554532527923584 -- g_loss: 0.33349865674972534
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Epoch: 15 -- Step: 23810 -- d_loss: 0.9289312958717346 -- g_loss: 0.8866093158721924 Epoch: 15 -- Step: 23820 -- d_loss: 1.1230885982513428 -- g_loss: 0.7690287232398987 Epoch: 15 -- Step: 23830 -- d_loss: 1.1312675476074219 -- g_loss: 0.9867594838142395 Epoch: 15 -- Step: 23840 -- d_loss: 1.045823097229004 -- g_loss: 0.9248620271682739 Epoch: 15 -- Step: 23850 -- d_loss: 0.9194333553314209 -- g_loss: 1.0671184062957764 Epoch: 15 -- Step: 23860 -- d_loss: 0.8052334785461426 -- g_loss: 1.003551959991455 Epoch: 15 -- Step: 23870 -- d_loss: 1.2835264205932617 -- g_loss: 1.9505250453948975 Epoch: 15 -- Step: 23880 -- d_loss: 1.0876357555389404 -- g_loss: 0.6902509927749634 Epoch: 15 -- Step: 23890 -- d_loss: 0.7137979865074158 -- g_loss: 1.3375630378723145 Epoch: 15 -- Step: 23900 -- d_loss: 1.032480001449585 -- g_loss: 1.1210482120513916
Epoch: 15 -- Step: 23910 -- d_loss: 0.8101462125778198 -- g_loss: 1.0299408435821533 Epoch: 15 -- Step: 23920 -- d_loss: 0.5324232578277588 -- g_loss: 1.633208990097046 Epoch: 15 -- Step: 23930 -- d_loss: 1.0261039733886719 -- g_loss: 0.6785948872566223 Epoch: 15 -- Step: 23940 -- d_loss: 1.1633551120758057 -- g_loss: 0.7134714722633362 Epoch: 15 -- Step: 23950 -- d_loss: 1.3863155841827393 -- g_loss: 0.45465466380119324 Epoch: 15 -- Step: 23960 -- d_loss: 0.9419220685958862 -- g_loss: 0.7947129011154175 Epoch: 15 -- Step: 23970 -- d_loss: 2.33543062210083 -- g_loss: 0.15089182555675507 Epoch: 15 -- Step: 23980 -- d_loss: 0.8982231020927429 -- g_loss: 0.9104741811752319 Epoch: 15 -- Step: 23990 -- d_loss: 1.8707597255706787 -- g_loss: 0.2800764739513397 Epoch: 15 -- Step: 24000 -- d_loss: 1.847388744354248 -- g_loss: 0.24694402515888214
Epoch: 15 -- Step: 24010 -- d_loss: 0.572848379611969 -- g_loss: 1.6777527332305908 Epoch: 15 -- Step: 24020 -- d_loss: 0.8467617034912109 -- g_loss: 0.82893306016922 Epoch: 15 -- Step: 24030 -- d_loss: 1.3128712177276611 -- g_loss: 0.46439439058303833 Epoch: 15 -- Step: 24040 -- d_loss: 1.0447368621826172 -- g_loss: 0.8072083592414856 Epoch: 15 -- Step: 24050 -- d_loss: 0.9914768934249878 -- g_loss: 0.827174961566925 Epoch: 15 -- Step: 24060 -- d_loss: 1.3415553569793701 -- g_loss: 0.4469711482524872 Epoch: 15 -- Step: 24070 -- d_loss: 1.005927562713623 -- g_loss: 0.9885238409042358 Epoch: 15 -- Step: 24080 -- d_loss: 0.8510338068008423 -- g_loss: 0.8194538354873657 Epoch: 15 -- Step: 24090 -- d_loss: 0.8888642191886902 -- g_loss: 0.8485872745513916 Epoch: 15 -- Step: 24100 -- d_loss: 0.8821150064468384 -- g_loss: 1.3478320837020874
Epoch: 15 -- Step: 24110 -- d_loss: 2.0292420387268066 -- g_loss: 0.18221993744373322 Epoch: 15 -- Step: 24120 -- d_loss: 0.9087895154953003 -- g_loss: 1.2692344188690186 Epoch: 15 -- Step: 24130 -- d_loss: 0.9034414291381836 -- g_loss: 1.1135916709899902 Epoch: 15 -- Step: 24140 -- d_loss: 1.0744593143463135 -- g_loss: 0.7988558411598206 Epoch: 15 -- Step: 24150 -- d_loss: 1.0432424545288086 -- g_loss: 0.6606248617172241 Epoch: 15 -- Step: 24160 -- d_loss: 1.2672474384307861 -- g_loss: 0.8295559883117676 Epoch: 15 -- Step: 24170 -- d_loss: 1.2213352918624878 -- g_loss: 0.7388330101966858 Epoch: 15 -- Step: 24180 -- d_loss: 1.1775364875793457 -- g_loss: 0.6337569355964661 Epoch: 15 -- Step: 24190 -- d_loss: 0.9700690507888794 -- g_loss: 1.2785178422927856 Epoch: 15 -- Step: 24200 -- d_loss: 1.0775401592254639 -- g_loss: 0.6211099624633789
Epoch: 15 -- Step: 24210 -- d_loss: 0.8330669403076172 -- g_loss: 1.7444508075714111 Epoch: 15 -- Step: 24220 -- d_loss: 0.7461210489273071 -- g_loss: 1.8015486001968384 Epoch: 15 -- Step: 24230 -- d_loss: 1.0293073654174805 -- g_loss: 1.2877907752990723 Epoch: 15 -- Step: 24240 -- d_loss: 0.985297679901123 -- g_loss: 1.003273606300354 Epoch: 15 -- Step: 24250 -- d_loss: 0.8321716785430908 -- g_loss: 1.2445646524429321 Epoch: 15 -- Step: 24260 -- d_loss: 1.065256118774414 -- g_loss: 0.5892586708068848 Epoch: 15 -- Step: 24270 -- d_loss: 0.9648717641830444 -- g_loss: 0.702539324760437 Epoch: 15 -- Step: 24280 -- d_loss: 1.2938885688781738 -- g_loss: 0.45888465642929077 Epoch: 15 -- Step: 24290 -- d_loss: 1.2338277101516724 -- g_loss: 0.49714934825897217 Epoch: 15 -- Step: 24300 -- d_loss: 0.8994573354721069 -- g_loss: 0.8096399903297424
Epoch: 15 -- Step: 24310 -- d_loss: 1.1237496137619019 -- g_loss: 0.5929355621337891 Epoch: 15 -- Step: 24320 -- d_loss: 1.1670724153518677 -- g_loss: 0.5577690005302429 Epoch: 15 -- Step: 24330 -- d_loss: 1.1764600276947021 -- g_loss: 0.9987252950668335 Epoch: 15 -- Step: 24340 -- d_loss: 1.0058715343475342 -- g_loss: 0.7640132308006287 Epoch: 15 -- Step: 24350 -- d_loss: 1.1041193008422852 -- g_loss: 0.7062592506408691 Epoch: 15 -- Step: 24360 -- d_loss: 1.4487669467926025 -- g_loss: 0.40245842933654785 Epoch: 15 -- Step: 24370 -- d_loss: 1.4290655851364136 -- g_loss: 0.42175427079200745 Epoch: 15 -- Step: 24380 -- d_loss: 1.1511179208755493 -- g_loss: 0.5585284233093262 Epoch: 15 -- Step: 24390 -- d_loss: 0.8454204201698303 -- g_loss: 1.1189594268798828 Epoch: 15 -- Step: 24400 -- d_loss: 1.2536178827285767 -- g_loss: 0.4873427152633667
Epoch: 15 -- Step: 24410 -- d_loss: 1.2828799486160278 -- g_loss: 1.201526403427124 Epoch: 15 -- Step: 24420 -- d_loss: 1.7198208570480347 -- g_loss: 0.283766508102417 Epoch: 15 -- Step: 24430 -- d_loss: 1.852805733680725 -- g_loss: 0.24112805724143982 Epoch: 15 -- Step: 24440 -- d_loss: 1.0735373497009277 -- g_loss: 0.6396640539169312 Epoch: 15 -- Step: 24450 -- d_loss: 0.9563931822776794 -- g_loss: 1.0680460929870605 Epoch: 15 -- Step: 24460 -- d_loss: 1.2574632167816162 -- g_loss: 0.48393863439559937 Epoch: 15 -- Step: 24470 -- d_loss: 1.187479019165039 -- g_loss: 0.6336123943328857 Epoch: 15 -- Step: 24480 -- d_loss: 0.972359299659729 -- g_loss: 0.8204376697540283 Epoch: 15 -- Step: 24490 -- d_loss: 1.08229660987854 -- g_loss: 1.0422242879867554 Epoch: 15 -- Step: 24500 -- d_loss: 0.8365886211395264 -- g_loss: 1.4261252880096436
Epoch: 15 -- Step: 24510 -- d_loss: 1.112112045288086 -- g_loss: 0.6232346296310425 Epoch: 15 -- Step: 24520 -- d_loss: 0.9946482181549072 -- g_loss: 1.075858235359192 Epoch: 15 -- Step: 24530 -- d_loss: 0.922025203704834 -- g_loss: 0.7876173257827759 Epoch: 15 -- Step: 24540 -- d_loss: 0.7749990224838257 -- g_loss: 1.2529888153076172 Epoch: 15 -- Step: 24550 -- d_loss: 1.3428759574890137 -- g_loss: 0.48537737131118774 Epoch: 15 -- Step: 24560 -- d_loss: 1.029031753540039 -- g_loss: 0.7141762375831604 Epoch: 15 -- Step: 24570 -- d_loss: 1.2500972747802734 -- g_loss: 0.5694663524627686 Epoch: 15 -- Step: 24580 -- d_loss: 1.2894006967544556 -- g_loss: 2.137603282928467 Epoch: 15 -- Step: 24590 -- d_loss: 1.1648396253585815 -- g_loss: 0.6410459280014038 Epoch: 15 -- Step: 24600 -- d_loss: 1.2594634294509888 -- g_loss: 0.6565822958946228
Epoch: 15 -- Step: 24610 -- d_loss: 0.7565124034881592 -- g_loss: 1.125654935836792 Epoch: 15 -- Step: 24620 -- d_loss: 0.8922339677810669 -- g_loss: 1.0131720304489136 Epoch: 15 -- Step: 24630 -- d_loss: 1.3520338535308838 -- g_loss: 0.4864788055419922 Epoch: 15 -- Step: 24640 -- d_loss: 1.2237167358398438 -- g_loss: 0.5338732600212097 Epoch: 15 -- Step: 24650 -- d_loss: 1.2535303831100464 -- g_loss: 0.9719611406326294 Epoch: 15 -- Step: 24660 -- d_loss: 0.9098912477493286 -- g_loss: 1.3870823383331299 Epoch: 15 -- Step: 24670 -- d_loss: 1.0639710426330566 -- g_loss: 0.6594957113265991 Epoch: 15 -- Step: 24680 -- d_loss: 1.2108056545257568 -- g_loss: 0.5576286315917969 Epoch: 15 -- Step: 24690 -- d_loss: 1.2018346786499023 -- g_loss: 0.5305285453796387 Epoch: 15 -- Step: 24700 -- d_loss: 0.8838063478469849 -- g_loss: 1.0401580333709717
Epoch: 15 -- Step: 24710 -- d_loss: 0.9199784994125366 -- g_loss: 0.9263631701469421 Epoch: 15 -- Step: 24720 -- d_loss: 1.3133437633514404 -- g_loss: 0.9226648807525635 Epoch: 15 -- Step: 24730 -- d_loss: 1.005791187286377 -- g_loss: 0.7111426591873169 Epoch: 15 -- Step: 24740 -- d_loss: 1.2057851552963257 -- g_loss: 0.5627543926239014 Epoch: 15 -- Step: 24750 -- d_loss: 1.074364423751831 -- g_loss: 0.6729726791381836 Epoch: 15 -- Step: 24760 -- d_loss: 0.791236162185669 -- g_loss: 1.261108160018921 Epoch: 15 -- Step: 24770 -- d_loss: 1.0319932699203491 -- g_loss: 0.7713086605072021 Epoch: 15 -- Step: 24780 -- d_loss: 1.2814810276031494 -- g_loss: 0.6602216362953186 Epoch: 15 -- Step: 24790 -- d_loss: 0.9085135459899902 -- g_loss: 0.9863538146018982 Epoch: 15 -- Step: 24800 -- d_loss: 1.0082772970199585 -- g_loss: 1.1972367763519287
Epoch: 15 -- Step: 24810 -- d_loss: 0.863767683506012 -- g_loss: 0.897164523601532 Epoch: 15 -- Step: 24820 -- d_loss: 0.9600186944007874 -- g_loss: 0.9586910009384155 Epoch: 15 -- Step: 24830 -- d_loss: 1.2039363384246826 -- g_loss: 0.5379837155342102 Epoch: 15 -- Step: 24840 -- d_loss: 0.9593901634216309 -- g_loss: 0.7436185479164124 Epoch: 15 -- Step: 24850 -- d_loss: 1.1127338409423828 -- g_loss: 0.5884783864021301 Epoch: 15 -- Step: 24860 -- d_loss: 1.231825828552246 -- g_loss: 0.5851010680198669 Epoch: 15 -- Step: 24870 -- d_loss: 1.0297163724899292 -- g_loss: 0.632841944694519 Epoch: 15 -- Step: 24880 -- d_loss: 0.9051352739334106 -- g_loss: 0.9894965887069702 Epoch: 15 -- Step: 24890 -- d_loss: 0.9563562870025635 -- g_loss: 0.7239680886268616 Epoch: 15 -- Step: 24900 -- d_loss: 0.8871372938156128 -- g_loss: 0.815345048904419
Epoch: 15 -- Step: 24910 -- d_loss: 1.1036131381988525 -- g_loss: 1.1617772579193115 Epoch: 15 -- Step: 24920 -- d_loss: 0.8550031781196594 -- g_loss: 0.8140701651573181 Epoch: 15 -- Step: 24930 -- d_loss: 0.9048609137535095 -- g_loss: 1.328948736190796 Epoch: 15 -- Step: 24940 -- d_loss: 0.9796388149261475 -- g_loss: 0.785921573638916 Epoch: 15 -- Step: 24950 -- d_loss: 1.038435697555542 -- g_loss: 0.6812708377838135 Epoch: 15 -- Step: 24960 -- d_loss: 1.0239582061767578 -- g_loss: 0.7060511112213135 Epoch: 15 -- Step: 24970 -- d_loss: 0.9053743481636047 -- g_loss: 0.9439504146575928 Epoch: 15 -- Step: 24980 -- d_loss: 0.8128654360771179 -- g_loss: 1.1015856266021729 Epoch: 15 -- Step: 24990 -- d_loss: 0.5115372538566589 -- g_loss: 1.8401585817337036 Epoch: 15 -- Step: 25000 -- d_loss: 0.9921500086784363 -- g_loss: 0.8285824656486511
Epoch: 15 -- Step: 25010 -- d_loss: 0.8461467027664185 -- g_loss: 0.9850324988365173 Epoch: 15 -- Step: 25020 -- d_loss: 1.4614992141723633 -- g_loss: 0.3613041043281555 Epoch: 15 -- Step: 25030 -- d_loss: 1.2032142877578735 -- g_loss: 1.3920702934265137 Epoch: 15 -- Step: 25040 -- d_loss: 0.9613320827484131 -- g_loss: 0.7161645889282227 Epoch: 15 -- Step: 25050 -- d_loss: 0.967689573764801 -- g_loss: 0.7386314868927002 Epoch: 15 -- Step: 25060 -- d_loss: 1.1411621570587158 -- g_loss: 0.5814177989959717 Epoch: 15 -- Step: 25070 -- d_loss: 0.9616973400115967 -- g_loss: 0.6591612100601196 Epoch: 15 -- Step: 25080 -- d_loss: 0.8126629590988159 -- g_loss: 1.0255327224731445 Epoch: 15 -- Step: 25090 -- d_loss: 1.2445869445800781 -- g_loss: 0.5137262940406799 Epoch: 15 -- Step: 25100 -- d_loss: 0.91463702917099 -- g_loss: 0.827133297920227
Epoch: 15 -- Step: 25110 -- d_loss: 1.0847264528274536 -- g_loss: 0.7816528677940369 Epoch: 15 -- Step: 25120 -- d_loss: 0.961135745048523 -- g_loss: 0.7648869752883911 Epoch: 15 -- Step: 25130 -- d_loss: 1.004514455795288 -- g_loss: 0.736642599105835 Epoch: 15 -- Step: 25140 -- d_loss: 1.1268706321716309 -- g_loss: 1.7197867631912231 Epoch: 15 -- Step: 25150 -- d_loss: 1.2187483310699463 -- g_loss: 0.591936469078064 Epoch: 15 -- Step: 25160 -- d_loss: 1.3533998727798462 -- g_loss: 0.4397781491279602 Epoch: 15 -- Step: 25170 -- d_loss: 1.0381625890731812 -- g_loss: 0.8426415324211121 Epoch: 15 -- Step: 25180 -- d_loss: 1.251457929611206 -- g_loss: 0.5287143588066101 Epoch: 15 -- Step: 25190 -- d_loss: 1.397373914718628 -- g_loss: 0.4447740912437439 Epoch: 15 -- Step: 25200 -- d_loss: 0.988656759262085 -- g_loss: 0.6949239373207092
Epoch: 15 -- Step: 25210 -- d_loss: 1.0469410419464111 -- g_loss: 0.7554051876068115 Epoch: 15 -- Step: 25220 -- d_loss: 1.0154094696044922 -- g_loss: 0.8980920314788818 Epoch: 15 -- Step: 25230 -- d_loss: 1.0401668548583984 -- g_loss: 0.6709136962890625 Epoch: 15 -- Step: 25240 -- d_loss: 1.469015121459961 -- g_loss: 0.3582119345664978 Epoch: 15 -- Step: 25250 -- d_loss: 1.1706125736236572 -- g_loss: 0.5736014246940613 Epoch: 15 -- Step: 25260 -- d_loss: 1.0628026723861694 -- g_loss: 0.7547166347503662 Epoch: 15 -- Step: 25270 -- d_loss: 1.462671160697937 -- g_loss: 0.35808929800987244 Epoch: 15 -- Step: 25280 -- d_loss: 0.9114338159561157 -- g_loss: 0.9024322032928467 Epoch: 15 -- Step: 25290 -- d_loss: 1.2231436967849731 -- g_loss: 0.48342761397361755 Epoch: 15 -- Step: 25300 -- d_loss: 1.1994837522506714 -- g_loss: 0.5249095559120178
Epoch: 15 -- Step: 25310 -- d_loss: 1.199954867362976 -- g_loss: 0.5013607740402222 Epoch: 16 -- Step: 25320 -- d_loss: 0.9428231716156006 -- g_loss: 0.737453043460846 Epoch: 16 -- Step: 25330 -- d_loss: 0.9785215258598328 -- g_loss: 0.7336521148681641 Epoch: 16 -- Step: 25340 -- d_loss: 0.8454701900482178 -- g_loss: 1.3573256731033325 Epoch: 16 -- Step: 25350 -- d_loss: 0.7501773834228516 -- g_loss: 1.4323147535324097 Epoch: 16 -- Step: 25360 -- d_loss: 0.7566165924072266 -- g_loss: 1.0586662292480469 Epoch: 16 -- Step: 25370 -- d_loss: 0.9317415356636047 -- g_loss: 0.7944796085357666 Epoch: 16 -- Step: 25380 -- d_loss: 1.4424967765808105 -- g_loss: 0.4113575220108032 Epoch: 16 -- Step: 25390 -- d_loss: 0.678371787071228 -- g_loss: 1.1035014390945435 Epoch: 16 -- Step: 25400 -- d_loss: 1.0764731168746948 -- g_loss: 0.6907122135162354
Epoch: 16 -- Step: 25410 -- d_loss: 1.1271090507507324 -- g_loss: 1.0778374671936035 Epoch: 16 -- Step: 25420 -- d_loss: 1.2057069540023804 -- g_loss: 0.5357669591903687 Epoch: 16 -- Step: 25430 -- d_loss: 1.096509575843811 -- g_loss: 0.659665048122406 Epoch: 16 -- Step: 25440 -- d_loss: 0.8889853954315186 -- g_loss: 0.7922021150588989 Epoch: 16 -- Step: 25450 -- d_loss: 0.9076262712478638 -- g_loss: 0.9864630699157715 Epoch: 16 -- Step: 25460 -- d_loss: 1.050191879272461 -- g_loss: 1.3114590644836426 Epoch: 16 -- Step: 25470 -- d_loss: 1.006084680557251 -- g_loss: 0.7679710984230042 Epoch: 16 -- Step: 25480 -- d_loss: 1.0741503238677979 -- g_loss: 0.8501994609832764 Epoch: 16 -- Step: 25490 -- d_loss: 1.0254353284835815 -- g_loss: 0.7038716077804565 Epoch: 16 -- Step: 25500 -- d_loss: 0.9989144802093506 -- g_loss: 0.8198792934417725
Epoch: 16 -- Step: 25510 -- d_loss: 0.9953700304031372 -- g_loss: 0.9130859375 Epoch: 16 -- Step: 25520 -- d_loss: 1.2153488397598267 -- g_loss: 0.5068455934524536 Epoch: 16 -- Step: 25530 -- d_loss: 1.5487642288208008 -- g_loss: 0.4038196802139282 Epoch: 16 -- Step: 25540 -- d_loss: 1.1473822593688965 -- g_loss: 0.6725240349769592 Epoch: 16 -- Step: 25550 -- d_loss: 0.8549702763557434 -- g_loss: 0.7871512174606323 Epoch: 16 -- Step: 25560 -- d_loss: 1.238434076309204 -- g_loss: 1.0675973892211914 Epoch: 16 -- Step: 25570 -- d_loss: 0.8793355226516724 -- g_loss: 1.1174393892288208 Epoch: 16 -- Step: 25580 -- d_loss: 0.9408241510391235 -- g_loss: 0.8154228925704956 Epoch: 16 -- Step: 25590 -- d_loss: 1.6416140794754028 -- g_loss: 0.32491615414619446 Epoch: 16 -- Step: 25600 -- d_loss: 1.2732799053192139 -- g_loss: 0.48371294140815735
Epoch: 16 -- Step: 25610 -- d_loss: 1.0207988023757935 -- g_loss: 1.0715394020080566 Epoch: 16 -- Step: 25620 -- d_loss: 1.0904300212860107 -- g_loss: 0.6611238718032837 Epoch: 16 -- Step: 25630 -- d_loss: 0.9124812483787537 -- g_loss: 1.0828907489776611 Epoch: 16 -- Step: 25640 -- d_loss: 0.7994741797447205 -- g_loss: 0.9076188802719116 Epoch: 16 -- Step: 25650 -- d_loss: 1.6319395303726196 -- g_loss: 0.29608768224716187 Epoch: 16 -- Step: 25660 -- d_loss: 0.9371896982192993 -- g_loss: 0.9312670230865479 Epoch: 16 -- Step: 25670 -- d_loss: 1.3370912075042725 -- g_loss: 0.4485197365283966 Epoch: 16 -- Step: 25680 -- d_loss: 1.323134422302246 -- g_loss: 0.4768795669078827 Epoch: 16 -- Step: 25690 -- d_loss: 1.2439732551574707 -- g_loss: 0.546252965927124 Epoch: 16 -- Step: 25700 -- d_loss: 0.5705269575119019 -- g_loss: 1.5959539413452148
Epoch: 16 -- Step: 25710 -- d_loss: 1.0081663131713867 -- g_loss: 0.6877402663230896 Epoch: 16 -- Step: 25720 -- d_loss: 1.1165392398834229 -- g_loss: 0.8410760760307312 Epoch: 16 -- Step: 25730 -- d_loss: 1.1685055494308472 -- g_loss: 0.563001275062561 Epoch: 16 -- Step: 25740 -- d_loss: 1.1499881744384766 -- g_loss: 0.5582711696624756 Epoch: 16 -- Step: 25750 -- d_loss: 0.762697696685791 -- g_loss: 0.9474097490310669 Epoch: 16 -- Step: 25760 -- d_loss: 1.2680513858795166 -- g_loss: 0.5490131378173828 Epoch: 16 -- Step: 25770 -- d_loss: 1.0619616508483887 -- g_loss: 0.6158775091171265 Epoch: 16 -- Step: 25780 -- d_loss: 0.7197357416152954 -- g_loss: 1.052496314048767 Epoch: 16 -- Step: 25790 -- d_loss: 0.8612603545188904 -- g_loss: 0.9952468872070312 Epoch: 16 -- Step: 25800 -- d_loss: 0.8953145146369934 -- g_loss: 0.9137580394744873
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Epoch: 16 -- Step: 26610 -- d_loss: 0.9299304485321045 -- g_loss: 0.9532334804534912 Epoch: 16 -- Step: 26620 -- d_loss: 1.2865349054336548 -- g_loss: 0.6918333768844604 Epoch: 16 -- Step: 26630 -- d_loss: 1.0568513870239258 -- g_loss: 1.1541297435760498 Epoch: 16 -- Step: 26640 -- d_loss: 0.9690059423446655 -- g_loss: 1.1636145114898682 Epoch: 16 -- Step: 26650 -- d_loss: 0.7679618000984192 -- g_loss: 0.9558489322662354 Epoch: 16 -- Step: 26660 -- d_loss: 1.2692780494689941 -- g_loss: 0.5386494398117065 Epoch: 16 -- Step: 26670 -- d_loss: 0.8124270439147949 -- g_loss: 1.6386810541152954 Epoch: 16 -- Step: 26680 -- d_loss: 1.263780951499939 -- g_loss: 0.46104198694229126 Epoch: 16 -- Step: 26690 -- d_loss: 1.2003333568572998 -- g_loss: 1.1049489974975586 Epoch: 16 -- Step: 26700 -- d_loss: 1.066990613937378 -- g_loss: 0.6290208101272583
Epoch: 16 -- Step: 26710 -- d_loss: 1.128418207168579 -- g_loss: 0.7604244947433472 Epoch: 16 -- Step: 26720 -- d_loss: 0.9795117378234863 -- g_loss: 0.8949793577194214 Epoch: 16 -- Step: 26730 -- d_loss: 0.7893458604812622 -- g_loss: 1.1259536743164062 Epoch: 16 -- Step: 26740 -- d_loss: 0.7658581137657166 -- g_loss: 0.8659433126449585 Epoch: 16 -- Step: 26750 -- d_loss: 1.4149349927902222 -- g_loss: 0.4067462682723999 Epoch: 16 -- Step: 26760 -- d_loss: 0.9636608958244324 -- g_loss: 1.3869531154632568 Epoch: 16 -- Step: 26770 -- d_loss: 1.17167329788208 -- g_loss: 0.5752176642417908 Epoch: 16 -- Step: 26780 -- d_loss: 0.9605655670166016 -- g_loss: 0.9598818421363831 Epoch: 16 -- Step: 26790 -- d_loss: 1.240977168083191 -- g_loss: 0.5454578995704651 Epoch: 16 -- Step: 26800 -- d_loss: 0.9735231399536133 -- g_loss: 0.804394006729126
Epoch: 16 -- Step: 26810 -- d_loss: 1.1989339590072632 -- g_loss: 0.540631890296936 Epoch: 16 -- Step: 26820 -- d_loss: 1.025977373123169 -- g_loss: 0.6488333940505981 Epoch: 16 -- Step: 26830 -- d_loss: 1.2238194942474365 -- g_loss: 0.7852591276168823 Epoch: 16 -- Step: 26840 -- d_loss: 0.9350297451019287 -- g_loss: 0.8919783234596252 Epoch: 16 -- Step: 26850 -- d_loss: 0.7606116533279419 -- g_loss: 1.2046781778335571 Epoch: 16 -- Step: 26860 -- d_loss: 1.0424110889434814 -- g_loss: 0.8290156126022339 Epoch: 16 -- Step: 26870 -- d_loss: 1.0457184314727783 -- g_loss: 0.6492207050323486 Epoch: 16 -- Step: 26880 -- d_loss: 1.762007713317871 -- g_loss: 2.832287311553955 Epoch: 16 -- Step: 26890 -- d_loss: 0.914118766784668 -- g_loss: 0.8117431402206421 Epoch: 17 -- Step: 26900 -- d_loss: 0.8199107646942139 -- g_loss: 0.9006682634353638
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Epoch: 17 -- Step: 27010 -- d_loss: 0.9364466667175293 -- g_loss: 0.854080319404602 Epoch: 17 -- Step: 27020 -- d_loss: 1.2562763690948486 -- g_loss: 0.5615617632865906 Epoch: 17 -- Step: 27030 -- d_loss: 1.1192954778671265 -- g_loss: 0.7361968159675598 Epoch: 17 -- Step: 27040 -- d_loss: 1.059119701385498 -- g_loss: 0.755614161491394 Epoch: 17 -- Step: 27050 -- d_loss: 1.1571769714355469 -- g_loss: 0.6600302457809448 Epoch: 17 -- Step: 27060 -- d_loss: 1.2096056938171387 -- g_loss: 0.6107839941978455 Epoch: 17 -- Step: 27070 -- d_loss: 1.1188228130340576 -- g_loss: 1.0231847763061523 Epoch: 17 -- Step: 27080 -- d_loss: 0.778934121131897 -- g_loss: 0.9928176999092102 Epoch: 17 -- Step: 27090 -- d_loss: 1.0066685676574707 -- g_loss: 0.9031915664672852 Epoch: 17 -- Step: 27100 -- d_loss: 1.058464765548706 -- g_loss: 0.7915732860565186
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Epoch: 18 -- Step: 29810 -- d_loss: 1.0229722261428833 -- g_loss: 0.987236738204956 Epoch: 18 -- Step: 29820 -- d_loss: 1.0585925579071045 -- g_loss: 0.6882251501083374 Epoch: 18 -- Step: 29830 -- d_loss: 1.1721960306167603 -- g_loss: 0.4954894185066223 Epoch: 18 -- Step: 29840 -- d_loss: 1.049485683441162 -- g_loss: 0.6482893824577332 Epoch: 18 -- Step: 29850 -- d_loss: 0.8563517332077026 -- g_loss: 0.8613877892494202 Epoch: 18 -- Step: 29860 -- d_loss: 1.1896681785583496 -- g_loss: 0.5699937343597412 Epoch: 18 -- Step: 29870 -- d_loss: 1.2652037143707275 -- g_loss: 0.49615705013275146 Epoch: 18 -- Step: 29880 -- d_loss: 1.1612039804458618 -- g_loss: 0.5463308095932007 Epoch: 18 -- Step: 29890 -- d_loss: 1.0013458728790283 -- g_loss: 0.8013399839401245 Epoch: 18 -- Step: 29900 -- d_loss: 0.9560822248458862 -- g_loss: 0.7276448607444763
Epoch: 18 -- Step: 29910 -- d_loss: 1.0987603664398193 -- g_loss: 0.8683469891548157 Epoch: 18 -- Step: 29920 -- d_loss: 1.2997322082519531 -- g_loss: 0.524434506893158 Epoch: 18 -- Step: 29930 -- d_loss: 0.8960838913917542 -- g_loss: 1.3563029766082764 Epoch: 18 -- Step: 29940 -- d_loss: 0.7851567268371582 -- g_loss: 0.8549400568008423 Epoch: 18 -- Step: 29950 -- d_loss: 0.8942896127700806 -- g_loss: 0.7992531061172485 Epoch: 18 -- Step: 29960 -- d_loss: 1.199954867362976 -- g_loss: 0.6193931698799133 Epoch: 18 -- Step: 29970 -- d_loss: 1.3099021911621094 -- g_loss: 1.3279635906219482 Epoch: 18 -- Step: 29980 -- d_loss: 1.0910921096801758 -- g_loss: 0.7264135479927063 Epoch: 18 -- Step: 29990 -- d_loss: 1.1375609636306763 -- g_loss: 0.9697721600532532 Epoch: 18 -- Step: 30000 -- d_loss: 1.0942121744155884 -- g_loss: 0.658065915107727
Epoch: 18 -- Step: 30010 -- d_loss: 1.6222960948944092 -- g_loss: 0.33017557859420776 Epoch: 18 -- Step: 30020 -- d_loss: 1.2245997190475464 -- g_loss: 0.6800923347473145 Epoch: 18 -- Step: 30030 -- d_loss: 0.7801588773727417 -- g_loss: 1.2721872329711914 Epoch: 18 -- Step: 30040 -- d_loss: 0.7545886039733887 -- g_loss: 1.0949280261993408 Epoch: 18 -- Step: 30050 -- d_loss: 1.014897108078003 -- g_loss: 0.7425205707550049 Epoch: 19 -- Step: 30060 -- d_loss: 1.095610499382019 -- g_loss: 0.6012181043624878 Epoch: 19 -- Step: 30070 -- d_loss: 1.1284732818603516 -- g_loss: 0.7168375849723816 Epoch: 19 -- Step: 30080 -- d_loss: 0.7608145475387573 -- g_loss: 1.1248562335968018 Epoch: 19 -- Step: 30090 -- d_loss: 1.074550986289978 -- g_loss: 0.6591218709945679 Epoch: 19 -- Step: 30100 -- d_loss: 1.0891172885894775 -- g_loss: 0.7871477603912354
Epoch: 19 -- Step: 30110 -- d_loss: 1.4976840019226074 -- g_loss: 0.3522650897502899 Epoch: 19 -- Step: 30120 -- d_loss: 0.9515120983123779 -- g_loss: 1.0326236486434937 Epoch: 19 -- Step: 30130 -- d_loss: 1.4654531478881836 -- g_loss: 0.3878292739391327 Epoch: 19 -- Step: 30140 -- d_loss: 1.453194260597229 -- g_loss: 0.37111663818359375 Epoch: 19 -- Step: 30150 -- d_loss: 0.9212074279785156 -- g_loss: 1.8142937421798706 Epoch: 19 -- Step: 30160 -- d_loss: 1.1228760480880737 -- g_loss: 0.5984090566635132 Epoch: 19 -- Step: 30170 -- d_loss: 0.9948022961616516 -- g_loss: 0.7219295501708984 Epoch: 19 -- Step: 30180 -- d_loss: 1.3658674955368042 -- g_loss: 0.8844482898712158 Epoch: 19 -- Step: 30190 -- d_loss: 0.8870265483856201 -- g_loss: 0.9099017381668091 Epoch: 19 -- Step: 30200 -- d_loss: 1.3891472816467285 -- g_loss: 0.40981507301330566
Epoch: 19 -- Step: 30210 -- d_loss: 1.1130990982055664 -- g_loss: 1.0429456233978271 Epoch: 19 -- Step: 30220 -- d_loss: 1.0832527875900269 -- g_loss: 0.6673470735549927 Epoch: 19 -- Step: 30230 -- d_loss: 1.1811797618865967 -- g_loss: 0.6408485770225525 Epoch: 19 -- Step: 30240 -- d_loss: 1.0683047771453857 -- g_loss: 0.8156595230102539 Epoch: 19 -- Step: 30250 -- d_loss: 1.0975573062896729 -- g_loss: 0.9064791202545166 Epoch: 19 -- Step: 30260 -- d_loss: 0.8531836271286011 -- g_loss: 0.7963806390762329 Epoch: 19 -- Step: 30270 -- d_loss: 1.212620735168457 -- g_loss: 0.5913405418395996 Epoch: 19 -- Step: 30280 -- d_loss: 0.6056879758834839 -- g_loss: 1.3677163124084473 Epoch: 19 -- Step: 30290 -- d_loss: 1.4345163106918335 -- g_loss: 0.42285260558128357 Epoch: 19 -- Step: 30300 -- d_loss: 1.0996811389923096 -- g_loss: 0.7508869767189026
Epoch: 19 -- Step: 30310 -- d_loss: 0.735696017742157 -- g_loss: 1.266808032989502 Epoch: 19 -- Step: 30320 -- d_loss: 0.7297035455703735 -- g_loss: 1.6326234340667725 Epoch: 19 -- Step: 30330 -- d_loss: 1.2600702047348022 -- g_loss: 0.5711162090301514 Epoch: 19 -- Step: 30340 -- d_loss: 1.0853780508041382 -- g_loss: 0.6013166904449463 Epoch: 19 -- Step: 30350 -- d_loss: 1.1719834804534912 -- g_loss: 0.5019471049308777 Epoch: 19 -- Step: 30360 -- d_loss: 1.02391517162323 -- g_loss: 0.7267873287200928 Epoch: 19 -- Step: 30370 -- d_loss: 0.7675670385360718 -- g_loss: 1.182678461074829 Epoch: 19 -- Step: 30380 -- d_loss: 0.6400880217552185 -- g_loss: 1.3464609384536743 Epoch: 19 -- Step: 30390 -- d_loss: 1.238656997680664 -- g_loss: 0.5925742387771606 Epoch: 19 -- Step: 30400 -- d_loss: 0.6333991289138794 -- g_loss: 2.213463306427002
Epoch: 19 -- Step: 30410 -- d_loss: 3.262573003768921 -- g_loss: 0.10781000554561615 Epoch: 19 -- Step: 30420 -- d_loss: 1.0863018035888672 -- g_loss: 0.7393444776535034 Epoch: 19 -- Step: 30430 -- d_loss: 1.1601152420043945 -- g_loss: 0.8022425174713135 Epoch: 19 -- Step: 30440 -- d_loss: 0.9117289781570435 -- g_loss: 0.8677211999893188 Epoch: 19 -- Step: 30450 -- d_loss: 1.5448768138885498 -- g_loss: 0.3874194025993347 Epoch: 19 -- Step: 30460 -- d_loss: 1.416698694229126 -- g_loss: 0.43567681312561035 Epoch: 19 -- Step: 30470 -- d_loss: 0.7874795198440552 -- g_loss: 1.314636468887329 Epoch: 19 -- Step: 30480 -- d_loss: 1.1441948413848877 -- g_loss: 0.7005046606063843 Epoch: 19 -- Step: 30490 -- d_loss: 0.7297247648239136 -- g_loss: 1.4733768701553345 Epoch: 19 -- Step: 30500 -- d_loss: 0.9320430755615234 -- g_loss: 0.8368313312530518
Epoch: 19 -- Step: 30510 -- d_loss: 1.0155489444732666 -- g_loss: 0.7239484190940857 Epoch: 19 -- Step: 30520 -- d_loss: 1.6781203746795654 -- g_loss: 0.29695796966552734 Epoch: 19 -- Step: 30530 -- d_loss: 1.0475953817367554 -- g_loss: 0.697159469127655 Epoch: 19 -- Step: 30540 -- d_loss: 0.7569607496261597 -- g_loss: 1.0722846984863281 Epoch: 19 -- Step: 30550 -- d_loss: 1.0448381900787354 -- g_loss: 0.6705777645111084 Epoch: 19 -- Step: 30560 -- d_loss: 0.7713499665260315 -- g_loss: 1.2473680973052979 Epoch: 19 -- Step: 30570 -- d_loss: 0.9711374640464783 -- g_loss: 0.6636261940002441 Epoch: 19 -- Step: 30580 -- d_loss: 0.788722574710846 -- g_loss: 1.6415514945983887 Epoch: 19 -- Step: 30590 -- d_loss: 1.0055418014526367 -- g_loss: 0.9026423692703247 Epoch: 19 -- Step: 30600 -- d_loss: 1.0037277936935425 -- g_loss: 0.7085118889808655
Epoch: 19 -- Step: 30610 -- d_loss: 1.127860188484192 -- g_loss: 0.8854708671569824 Epoch: 19 -- Step: 30620 -- d_loss: 0.8087499141693115 -- g_loss: 1.148797869682312 Epoch: 19 -- Step: 30630 -- d_loss: 0.9621930122375488 -- g_loss: 0.746660590171814 Epoch: 19 -- Step: 30640 -- d_loss: 1.1122691631317139 -- g_loss: 0.5753003358840942 Epoch: 19 -- Step: 30650 -- d_loss: 1.1526025533676147 -- g_loss: 0.5502333641052246 Epoch: 19 -- Step: 30660 -- d_loss: 1.5934966802597046 -- g_loss: 0.32701683044433594 Epoch: 19 -- Step: 30670 -- d_loss: 0.8600304126739502 -- g_loss: 0.9804549217224121 Epoch: 19 -- Step: 30680 -- d_loss: 0.991641640663147 -- g_loss: 0.9595404863357544 Epoch: 19 -- Step: 30690 -- d_loss: 1.1223927736282349 -- g_loss: 0.6562057733535767 Epoch: 19 -- Step: 30700 -- d_loss: 1.021412968635559 -- g_loss: 0.7403500080108643
Epoch: 19 -- Step: 30710 -- d_loss: 0.7591954469680786 -- g_loss: 1.0452194213867188 Epoch: 19 -- Step: 30720 -- d_loss: 0.9589138031005859 -- g_loss: 0.7455599904060364 Epoch: 19 -- Step: 30730 -- d_loss: 1.3164864778518677 -- g_loss: 0.45543158054351807 Epoch: 19 -- Step: 30740 -- d_loss: 0.9758843779563904 -- g_loss: 0.7303781509399414 Epoch: 19 -- Step: 30750 -- d_loss: 1.0638948678970337 -- g_loss: 0.6060296297073364 Epoch: 19 -- Step: 30760 -- d_loss: 0.9905366897583008 -- g_loss: 0.7115344405174255 Epoch: 19 -- Step: 30770 -- d_loss: 1.6672868728637695 -- g_loss: 0.3190387189388275 Epoch: 19 -- Step: 30780 -- d_loss: 1.5845115184783936 -- g_loss: 0.3283407688140869 Epoch: 19 -- Step: 30790 -- d_loss: 0.8920595645904541 -- g_loss: 0.9656873941421509 Epoch: 19 -- Step: 30800 -- d_loss: 1.1076974868774414 -- g_loss: 0.6979396343231201
Epoch: 19 -- Step: 30810 -- d_loss: 0.9728490114212036 -- g_loss: 0.8500254154205322 Epoch: 19 -- Step: 30820 -- d_loss: 1.4897565841674805 -- g_loss: 0.37175431847572327 Epoch: 19 -- Step: 30830 -- d_loss: 1.0054494142532349 -- g_loss: 0.7457753419876099 Epoch: 19 -- Step: 30840 -- d_loss: 0.8834091424942017 -- g_loss: 1.1583675146102905 Epoch: 19 -- Step: 30850 -- d_loss: 1.0075328350067139 -- g_loss: 0.7421847581863403 Epoch: 19 -- Step: 30860 -- d_loss: 0.8657203316688538 -- g_loss: 0.8463904857635498 Epoch: 19 -- Step: 30870 -- d_loss: 0.9986628293991089 -- g_loss: 0.6720513105392456 Epoch: 19 -- Step: 30880 -- d_loss: 1.1248706579208374 -- g_loss: 0.7873465418815613 Epoch: 19 -- Step: 30890 -- d_loss: 1.1525449752807617 -- g_loss: 0.5735503435134888 Epoch: 19 -- Step: 30900 -- d_loss: 1.0836267471313477 -- g_loss: 0.8143559694290161
Epoch: 19 -- Step: 30910 -- d_loss: 1.0604984760284424 -- g_loss: 0.7370425462722778 Epoch: 19 -- Step: 30920 -- d_loss: 1.3002103567123413 -- g_loss: 0.531073808670044 Epoch: 19 -- Step: 30930 -- d_loss: 0.7609569430351257 -- g_loss: 1.0693554878234863 Epoch: 19 -- Step: 30940 -- d_loss: 1.0657228231430054 -- g_loss: 0.7438643574714661 Epoch: 19 -- Step: 30950 -- d_loss: 0.6723084449768066 -- g_loss: 1.1502480506896973 Epoch: 19 -- Step: 30960 -- d_loss: 1.045762300491333 -- g_loss: 0.6893225908279419 Epoch: 19 -- Step: 30970 -- d_loss: 0.7308226823806763 -- g_loss: 1.2947752475738525 Epoch: 19 -- Step: 30980 -- d_loss: 1.5249097347259521 -- g_loss: 0.3584054708480835 Epoch: 19 -- Step: 30990 -- d_loss: 0.8313144445419312 -- g_loss: 1.3706566095352173 Epoch: 19 -- Step: 31000 -- d_loss: 1.1865209341049194 -- g_loss: 0.575059175491333
Epoch: 19 -- Step: 31010 -- d_loss: 0.8497049808502197 -- g_loss: 0.9198989272117615 Epoch: 19 -- Step: 31020 -- d_loss: 1.0778210163116455 -- g_loss: 1.1284372806549072 Epoch: 19 -- Step: 31030 -- d_loss: 1.478476881980896 -- g_loss: 0.412983238697052 Epoch: 19 -- Step: 31040 -- d_loss: 0.9577548503875732 -- g_loss: 0.7301385402679443 Epoch: 19 -- Step: 31050 -- d_loss: 1.1210249662399292 -- g_loss: 0.7413944602012634 Epoch: 19 -- Step: 31060 -- d_loss: 1.0379382371902466 -- g_loss: 0.8430309295654297 Epoch: 19 -- Step: 31070 -- d_loss: 1.106832504272461 -- g_loss: 0.6112980246543884 Epoch: 19 -- Step: 31080 -- d_loss: 0.958267867565155 -- g_loss: 1.0709015130996704 Epoch: 19 -- Step: 31090 -- d_loss: 1.3394780158996582 -- g_loss: 1.1321786642074585 Epoch: 19 -- Step: 31100 -- d_loss: 0.7937523722648621 -- g_loss: 1.084578275680542
Epoch: 19 -- Step: 31110 -- d_loss: 1.031166672706604 -- g_loss: 0.7405064105987549 Epoch: 19 -- Step: 31120 -- d_loss: 1.2912333011627197 -- g_loss: 0.48247766494750977 Epoch: 19 -- Step: 31130 -- d_loss: 0.9009449481964111 -- g_loss: 0.7917804718017578 Epoch: 19 -- Step: 31140 -- d_loss: 1.5228482484817505 -- g_loss: 0.35748612880706787 Epoch: 19 -- Step: 31150 -- d_loss: 1.3772677183151245 -- g_loss: 0.5299869179725647 Epoch: 19 -- Step: 31160 -- d_loss: 0.963721752166748 -- g_loss: 0.7732265591621399 Epoch: 19 -- Step: 31170 -- d_loss: 0.8148629665374756 -- g_loss: 1.1895830631256104 Epoch: 19 -- Step: 31180 -- d_loss: 0.6883808970451355 -- g_loss: 1.2476228475570679 Epoch: 19 -- Step: 31190 -- d_loss: 0.8763558864593506 -- g_loss: 0.9243912100791931 Epoch: 19 -- Step: 31200 -- d_loss: 1.279861569404602 -- g_loss: 0.5369360446929932
Epoch: 19 -- Step: 31210 -- d_loss: 0.7029823660850525 -- g_loss: 1.1793148517608643 Epoch: 19 -- Step: 31220 -- d_loss: 1.1455910205841064 -- g_loss: 0.5416557788848877 Epoch: 19 -- Step: 31230 -- d_loss: 1.285020112991333 -- g_loss: 0.46809282898902893 Epoch: 19 -- Step: 31240 -- d_loss: 1.2515170574188232 -- g_loss: 0.49971896409988403 Epoch: 19 -- Step: 31250 -- d_loss: 0.7883325219154358 -- g_loss: 1.0072897672653198 Epoch: 19 -- Step: 31260 -- d_loss: 1.0662455558776855 -- g_loss: 1.6368958950042725 Epoch: 19 -- Step: 31270 -- d_loss: 1.3618319034576416 -- g_loss: 0.4450606107711792 Epoch: 19 -- Step: 31280 -- d_loss: 0.864226222038269 -- g_loss: 0.8895485997200012 Epoch: 19 -- Step: 31290 -- d_loss: 0.8816205263137817 -- g_loss: 0.8394941091537476 Epoch: 19 -- Step: 31300 -- d_loss: 1.0276730060577393 -- g_loss: 0.7659559845924377
Epoch: 19 -- Step: 31310 -- d_loss: 0.9207336902618408 -- g_loss: 1.2490744590759277 Epoch: 19 -- Step: 31320 -- d_loss: 1.2726433277130127 -- g_loss: 0.5049201250076294 Epoch: 19 -- Step: 31330 -- d_loss: 1.0692529678344727 -- g_loss: 0.6452456116676331 Epoch: 19 -- Step: 31340 -- d_loss: 0.8201664686203003 -- g_loss: 0.9821822643280029 Epoch: 19 -- Step: 31350 -- d_loss: 1.1778345108032227 -- g_loss: 0.6171993613243103 Epoch: 19 -- Step: 31360 -- d_loss: 0.9410091042518616 -- g_loss: 0.7149861454963684 Epoch: 19 -- Step: 31370 -- d_loss: 1.0495260953903198 -- g_loss: 0.8843044638633728 Epoch: 19 -- Step: 31380 -- d_loss: 1.3296501636505127 -- g_loss: 0.44968119263648987 Epoch: 19 -- Step: 31390 -- d_loss: 1.0093899965286255 -- g_loss: 0.7678624391555786 Epoch: 19 -- Step: 31400 -- d_loss: 1.0771844387054443 -- g_loss: 0.6357223391532898
Epoch: 19 -- Step: 31410 -- d_loss: 0.9190840721130371 -- g_loss: 1.0156714916229248 Epoch: 19 -- Step: 31420 -- d_loss: 1.0696382522583008 -- g_loss: 0.9874459505081177 Epoch: 19 -- Step: 31430 -- d_loss: 0.9156660437583923 -- g_loss: 1.1460163593292236 Epoch: 19 -- Step: 31440 -- d_loss: 1.3221935033798218 -- g_loss: 0.48195189237594604 Epoch: 19 -- Step: 31450 -- d_loss: 1.2032725811004639 -- g_loss: 0.5315960645675659 Epoch: 19 -- Step: 31460 -- d_loss: 0.8936551809310913 -- g_loss: 1.409350872039795 Epoch: 19 -- Step: 31470 -- d_loss: 0.7267187833786011 -- g_loss: 1.1944221258163452 Epoch: 19 -- Step: 31480 -- d_loss: 0.8290855884552002 -- g_loss: 0.9786823391914368 Epoch: 19 -- Step: 31490 -- d_loss: 1.5071938037872314 -- g_loss: 0.3492473363876343 Epoch: 19 -- Step: 31500 -- d_loss: 0.847723662853241 -- g_loss: 1.1840665340423584
Epoch: 19 -- Step: 31510 -- d_loss: 0.8285590410232544 -- g_loss: 0.8786795139312744 Epoch: 19 -- Step: 31520 -- d_loss: 0.8073294162750244 -- g_loss: 0.9948317408561707 Epoch: 19 -- Step: 31530 -- d_loss: 1.122805118560791 -- g_loss: 0.5898324251174927 Epoch: 19 -- Step: 31540 -- d_loss: 1.0461024045944214 -- g_loss: 0.6499029994010925 Epoch: 19 -- Step: 31550 -- d_loss: 0.9889886379241943 -- g_loss: 0.8837471008300781 Epoch: 19 -- Step: 31560 -- d_loss: 0.8612461090087891 -- g_loss: 1.0388143062591553 Epoch: 19 -- Step: 31570 -- d_loss: 0.7741495370864868 -- g_loss: 0.9470747709274292 Epoch: 19 -- Step: 31580 -- d_loss: 0.9336786270141602 -- g_loss: 0.7748023271560669 Epoch: 19 -- Step: 31590 -- d_loss: 0.7514257431030273 -- g_loss: 1.1701009273529053 Epoch: 19 -- Step: 31600 -- d_loss: 1.1196757555007935 -- g_loss: 0.6376916170120239
Epoch: 19 -- Step: 31610 -- d_loss: 1.0578854084014893 -- g_loss: 0.6767321825027466 Epoch: 19 -- Step: 31620 -- d_loss: 1.1476221084594727 -- g_loss: 0.7038830518722534 Epoch: 19 -- Step: 31630 -- d_loss: 1.267543911933899 -- g_loss: 0.5084195137023926 Epoch: 19 -- Step: 31640 -- d_loss: 0.7961710095405579 -- g_loss: 1.24009108543396
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.